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
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation Presents a memory decomposition framework separating spatio-temporal beliefs to support autonomous robot navigation under evolving online goals. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic spatial navigation, not LLM planning or tool-use agents.
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation Presents a memory decomposition framework separating spatio-temporal beliefs to support autonomous robot navigation under evolving online goals. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Concerns quadruped robot navigation, not industrial defect inspection.
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation Presents a memory decomposition framework separating spatio-temporal beliefs to support autonomous robot navigation under evolving online goals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with robotic navigation beliefs, not AI safety or guardrails.
Learning How to Remember: Memory Modality Selection for GUI Agents Introduces SwitchMem, a routing system that dynamically selects between text and visual memory representations for vision-language GUI agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent memory routing, not deployment safety or guardrails.
Learning How to Remember: Memory Modality Selection for GUI Agents Introduces SwitchMem, a routing system that dynamically selects between text and visual memory representations for vision-language GUI agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an agent memory method, not general evaluation benchmarks.
MERIT: Multimodal Evidence Reasoning over Image Tables Proposes MERIT, a framework using visual table reasoning and adapted encoders to extract information directly from table images. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual table understanding, not multi-step agent planning.
MERIT: Multimodal Evidence Reasoning over Image Tables Proposes MERIT, a framework using visual table reasoning and adapted encoders to extract information directly from table images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on table images, not industrial inspection or defect detection.
MERIT: Multimodal Evidence Reasoning over Image Tables Proposes MERIT, a framework using visual table reasoning and adapted encoders to extract information directly from table images. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training framework for table reasoning, not an evaluation benchmark.
MERIT: Multimodal Evidence Reasoning over Image Tables Proposes MERIT, a framework using visual table reasoning and adapted encoders to extract information directly from table images. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual table understanding, not deployment safety or red-teaming.
AI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study The clinical study compares hyperspectral and RGB imaging for intraoperative detection of low-grade gliomas during brain tumor surgery. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses surgical tumor classification, not autonomous language agents.
AI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study The clinical study compares hyperspectral and RGB imaging for intraoperative detection of low-grade gliomas during brain tumor surgery. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Deals with clinical brain surgery imaging, not industrial manufacturing.
AI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study The clinical study compares hyperspectral and RGB imaging for intraoperative detection of low-grade gliomas during brain tumor surgery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Pertains to surgical image classification, not AI deployment safety.
AI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study The clinical study compares hyperspectral and RGB imaging for intraoperative detection of low-grade gliomas during brain tumor surgery. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on hyperspectral medical imaging, not multimodal language models.
AI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study The clinical study compares hyperspectral and RGB imaging for intraoperative detection of low-grade gliomas during brain tumor surgery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical glioma classification, not LLM benchmarks.
HaloBlock: Faster and Better Decoding for Diffusion Large Language Models HaloBlock allocates adaptive context halo regions across blocks to smooth context transitions and improve diffusion language model decoding. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text diffusion LLMs, not multimodal architectures.
HaloBlock: Faster and Better Decoding for Diffusion Large Language Models HaloBlock allocates adaptive context halo regions across blocks to smooth context transitions and improve diffusion language model decoding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an inference decoding mechanism, not an evaluation framework.
HaloBlock: Faster and Better Decoding for Diffusion Large Language Models HaloBlock allocates adaptive context halo regions across blocks to smooth context transitions and improve diffusion language model decoding. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on decoding algorithms for diffusion LLMs, not planning agents.
HaloBlock: Faster and Better Decoding for Diffusion Large Language Models HaloBlock allocates adaptive context halo regions across blocks to smooth context transitions and improve diffusion language model decoding. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Concerns diffusion language model decoding, not document retrieval.
HaloBlock: Faster and Better Decoding for Diffusion Large Language Models HaloBlock allocates adaptive context halo regions across blocks to smooth context transitions and improve diffusion language model decoding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on decoding acceleration, not deployment safety or guardrails.
HaloBlock: Faster and Better Decoding for Diffusion Large Language Models HaloBlock allocates adaptive context halo regions across blocks to smooth context transitions and improve diffusion language model decoding. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates on reasoning/code but core contribution is general diffusion LLM decoding.
FETALFusion: Resolution-Aware State-Space Modeling for Multi-Domain Fetal Head Segmentation FETALFusion combines CNN and Mamba architectures to segment fetal heads accurately across different ultrasound acquisition methods. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical image segmentation, not language model evaluation.
FETALFusion: Resolution-Aware State-Space Modeling for Multi-Domain Fetal Head Segmentation FETALFusion combines CNN and Mamba architectures to segment fetal heads accurately across different ultrasound acquisition methods. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a medical segmentation model, not LLM agents.
FETALFusion: Resolution-Aware State-Space Modeling for Multi-Domain Fetal Head Segmentation FETALFusion combines CNN and Mamba architectures to segment fetal heads accurately across different ultrasound acquisition methods. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on ultrasound image segmentation, not multimodal vision-language models.
FETALFusion: Resolution-Aware State-Space Modeling for Multi-Domain Fetal Head Segmentation FETALFusion combines CNN and Mamba architectures to segment fetal heads accurately across different ultrasound acquisition methods. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses medical imaging, not AI deployment safety or guardrails.
FETALFusion: Resolution-Aware State-Space Modeling for Multi-Domain Fetal Head Segmentation FETALFusion combines CNN and Mamba architectures to segment fetal heads accurately across different ultrasound acquisition methods. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses fetal ultrasound segmentation, not industrial manufacturing inspection.
Virtual Head Attention Virtual Head Attention uses two linear operations to compress key-value cache overhead without increasing training compute or sacrificing output quality. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses core transformer attention mechanics, not software engineering.
Virtual Head Attention Virtual Head Attention uses two linear operations to compress key-value cache overhead without increasing training compute or sacrificing output quality. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an architectural design, not an LLM benchmark.
Virtual Head Attention Virtual Head Attention uses two linear operations to compress key-value cache overhead without increasing training compute or sacrificing output quality. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes an attention mechanism, not agent architectures or planning.
Virtual Head Attention Virtual Head Attention uses two linear operations to compress key-value cache overhead without increasing training compute or sacrificing output quality. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Concerns general transformer attention, not multimodal-specific processing.
Virtual Head Attention Virtual Head Attention uses two linear operations to compress key-value cache overhead without increasing training compute or sacrificing output quality. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on attention architecture for KV cache, not retrieval-augmented generation.
Virtual Head Attention Virtual Head Attention uses two linear operations to compress key-value cache overhead without increasing training compute or sacrificing output quality. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on attention efficiency, not AI safety or guardrails.
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling The paper proposes a shopping basket choice model using specialized negative sampling to capture product interactions from retail transactions. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on retail basket choice modeling, not LLM agents.
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling The paper proposes a shopping basket choice model using specialized negative sampling to capture product interactions from retail transactions. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses product interaction modeling, not AI deployment safety.
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling The paper proposes a shopping basket choice model using specialized negative sampling to capture product interactions from retail transactions. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on retail recommendation embeddings, not small language models or inference.
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling The paper proposes a shopping basket choice model using specialized negative sampling to capture product interactions from retail transactions. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Concerns basket choice embeddings, not LLM evaluation.
BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation BELT compresses key-value caches using attention-distribution alignment and learned global rank allocation across model heads. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a compression framework, not an evaluation benchmark.
BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation BELT compresses key-value caches using attention-distribution alignment and learned global rank allocation across model heads. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns cache optimization rather than safety, jailbreaks, or hallucination.
BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation BELT compresses key-value caches using attention-distribution alignment and learned global rank allocation across model heads. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on KV cache reduction, not multi-step agent planning.
Seeing Depth but Choosing Cues in Vision-Language Models The paper analyzes how vision-language models favor 2D image cues over internal 3D depth representations when judging relative distances. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates visual depth perception heuristics, not deployment safety or guardrails.
Seeing Depth but Choosing Cues in Vision-Language Models The paper analyzes how vision-language models favor 2D image cues over internal 3D depth representations when judging relative distances. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies perception in VLMs, not agent planning or tool use.
Seeing Depth but Choosing Cues in Vision-Language Models The paper analyzes how vision-language models favor 2D image cues over internal 3D depth representations when judging relative distances. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes depth cues and internal representations, not benchmark methodology.
Seeing Through the Forgery Is Not Enough: Regressive Sycophancy under Fabricated Evidence in Medical Vision-Language Models The authors show that medical vision-language models frequently abandon correct clinical answers when prompted with fabricated medical documents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Concerns sycophancy evaluation, not model compression or efficiency.
Seeing Through the Forgery Is Not Enough: Regressive Sycophancy under Fabricated Evidence in Medical Vision-Language Models The authors show that medical vision-language models frequently abandon correct clinical answers when prompted with fabricated medical documents. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates VLM sycophancy, not tool-using or planning agents.
Seeing Through the Forgery Is Not Enough: Regressive Sycophancy under Fabricated Evidence in Medical Vision-Language Models The authors show that medical vision-language models frequently abandon correct clinical answers when prompted with fabricated medical documents. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical vision-language models, not code LLMs.
Seeing Through the Forgery Is Not Enough: Regressive Sycophancy under Fabricated Evidence in Medical Vision-Language Models The authors show that medical vision-language models frequently abandon correct clinical answers when prompted with fabricated medical documents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on sycophantic alignment to fake evidence, not RAG systems.
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks The paper presents a saliency-guided patch replay method to mitigate catastrophic forgetting in spiking neural networks under tight memory budgets. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with neuromorphic vision networks, not multimodal foundation models.
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks The paper presents a saliency-guided patch replay method to mitigate catastrophic forgetting in spiking neural networks under tight memory budgets. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on continual learning in spiking neural networks, not agents.
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks The paper presents a saliency-guided patch replay method to mitigate catastrophic forgetting in spiking neural networks under tight memory budgets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a replay strategy rather than LLM benchmarking.
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks The paper presents a saliency-guided patch replay method to mitigate catastrophic forgetting in spiking neural networks under tight memory budgets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns continual learning replay, not AI deployment safety or robustness.
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks The paper presents a saliency-guided patch replay method to mitigate catastrophic forgetting in spiking neural networks under tight memory budgets. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluated on CIFAR datasets, not industrial defect detection or manufacturing.
Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models The paper proposes predictability-aware supervised fine-tuning to resolve training and inference order mismatches in diffusion language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion model SFT, not deployment safety or guardrails.
Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models The paper proposes predictability-aware supervised fine-tuning to resolve training and inference order mismatches in diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training method, not a benchmark or evaluation framework.
Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models The paper proposes predictability-aware supervised fine-tuning to resolve training and inference order mismatches in diffusion language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Concerns training diffusion language models, not autonomous planning agents.
Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models The paper proposes predictability-aware supervised fine-tuning to resolve training and inference order mismatches in diffusion language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion model training dynamics, not model compression or serving.
Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models The paper proposes predictability-aware supervised fine-tuning to resolve training and inference order mismatches in diffusion language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Tests on code benchmarks, but the core contribution is general DLM fine-tuning.
Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models The paper proposes predictability-aware supervised fine-tuning to resolve training and inference order mismatches in diffusion language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on fine-tuning diffusion language models, not document retrieval.
Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models The paper proposes predictability-aware supervised fine-tuning to resolve training and inference order mismatches in diffusion language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with diffusion language models, not multimodal vision-language models.
SWEPatchTest: Evaluating Software Tests Beyond Reproduction SWEPatchTest provides a benchmark to evaluate whether agent-generated test suites correctly accept valid patches and reject invalid ones. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on evaluating generated software test suites, not general agent frameworks.
SWEPatchTest: Evaluating Software Tests Beyond Reproduction SWEPatchTest provides a benchmark to evaluate whether agent-generated test suites correctly accept valid patches and reject invalid ones. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on test-suite benchmarking rather than deployment safety or jailbreaks.
Cost-Efficient Privacy Enforcement for Email-Generating LLM Agents under Strategic Information Requests The paper introduces local contract-conditioned rewriting to prevent sensitive data leakage in email-generating language model agents cost-effectively. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an enforcement framework rather than general LLM evaluation benchmarks.

13,741–13,800 of 17,313← Previous Next →