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
Latent Core Tokenizer: Compress, but Meaningfully Introduces a multilingual tokenizer that uses structural discovery and linguistic constraints to improve downstream model representations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates tokenizer construction methods rather than multi-step LLM agent systems.
Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search Presents a framework that uses language models offline to generate executable feature-engineering code for tabular risk models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses industrial risk-control modeling rather than deployment safety, jailbreaks, or alignment.
Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search Presents a framework that uses language models offline to generate executable feature-engineering code for tabular risk models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text processing and program generation without multimodal inputs.
Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search Presents a framework that uses language models offline to generate executable feature-engineering code for tabular risk models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Generates offline feature-engineering programs from text without performing retrieval-augmented generation.
Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search Presents a framework that uses language models offline to generate executable feature-engineering code for tabular risk models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on program synthesis for feature extraction rather than model compression or efficient serving.
Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search Presents a framework that uses language models offline to generate executable feature-engineering code for tabular risk models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a feature engineering method rather than an LLM evaluation framework or benchmark.
SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Evaluates spatial reasoning accuracy rather than AI safety, guardrails, or jailbreaks.
SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address code generation, program repair, or software engineering tasks.
SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates predictive spatial reasoning in vision-language models, not multi-step tool-using autonomous agents.
SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on spatial reasoning in vision-language models rather than retrieval-augmented generation systems.
SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Benchmark covers general 3D scenes rather than industrial inspection or manufacturing computer vision.
SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on multimodal benchmark evaluation rather than model compression, quantization, or efficient serving.
ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills Proposes a framework that encodes visual trajectories into reusable skill cards to reinforce vision-language agent planning and rewards. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Tests on abstract game environments (e.g. Sokoban) rather than industrial vision inspection.
ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills Proposes a framework that encodes visual trajectories into reusable skill cards to reinforce vision-language agent planning and rewards. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on task completion performance rather than safety guardrails or alignment.
ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills Proposes a framework that encodes visual trajectories into reusable skill cards to reinforce vision-language agent planning and rewards. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on reinforcement learning policy skills rather than document retrieval or RAG.
ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills Proposes a framework that encodes visual trajectories into reusable skill cards to reinforce vision-language agent planning and rewards. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an agent framework and training method rather than an evaluation benchmark.
ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills Proposes a framework that encodes visual trajectories into reusable skill cards to reinforce vision-language agent planning and rewards. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates visual RL environments rather than programming or software code.
ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills Proposes a framework that encodes visual trajectories into reusable skill cards to reinforce vision-language agent planning and rewards. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on reinforcement learning sample efficiency rather than model compression or inference latency.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not study retrieval pipelines or retrieval-augmented generation.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on visual reasoning models rather than multi-step tool-using agents.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate model pruning, quantization, or efficient deployment serving.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Develops general visual world modeling rather than industrial quality control or inspection.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Investigates general spatial and physical reasoning rather than deployment safety or guardrails.
WOVEN: Weaving Visual World Modeling into Multimodal LLMs Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Studies multimodal physical and spatial reasoning, not software engineering or code.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address document retrieval or knowledge-augmented generation.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on video perception models, not autonomous planning or tool-using agents.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Investigates frame-rate trade-offs rather than model compression, quantization, or distillation.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates general video streaming rather than industrial manufacturing or defect inspection.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates dynamic video perception, entirely unrelated to software or coding.
FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams? Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on perception accuracy rather than safety risks or alignment.
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on code repair and bug injection, not retrieval-augmented generation.
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Operates on source code repositories and text rather than multimodal inputs.
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent coding competence rather than AI safety or guardrails.
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, distillation, or inference optimization.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on speculative decoding speedup rather than novel evaluation methodologies.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Develops a general speculative decoding mechanism rather than code-specific tools.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on inference speed rather than safety, security, or robustness guardrails.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies to autoregressive text generation without multimodal inputs.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not explore retrieval systems or knowledge-augmented architectures.
Contextual Correction of Parallel Draftsfor Speculative Decoding Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents an inference decoding algorithm, not an autonomous agent framework.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Processes text inputs exclusively for event induction.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses natural language event extraction rather than programming or software code.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on event extraction and ontology induction, not retrieval-augmented generation.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an unsupervised learning method, not a benchmark or evaluation framework.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes an unsupervised clustering framework, not an LLM agent with tools or planning.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not focus on AI safety, alignment, robustness, or deployment guardrails.
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Proposes an unsupervised framework using contrastive learning and clustering to induce, name, and annotate event types from text. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not explore model compression, quantization, or efficient inference serving.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a recurrent neural network architecture, not an autonomous agent system.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on computational efficiency and architecture rather than safety or alignment.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval mechanisms or augmented external knowledge.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an architecture rather than a benchmarking or evaluation methodology.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals strictly with language modeling architectures without multimodal components.
One Model to Loop Them All: Elastic Latent Recurrence for Language Models Presents a training method for latent recurrent language models that avoids performance penalties at single loops while scaling with compute. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Proposes a general language modeling architecture unrelated to coding or software engineering.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates deep-research citation agents, not coding or software development agents.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on prompt evolution for research agents rather than retrieval pipelines or indexing.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Explores prompt evolution rather than model distillation, pruning, or efficient inference.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses generalizability and overfitting rather than safety, alignment, or guardrails.
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Works entirely with text prompts and text-based research queries.
Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities Proposes a diagnostic framework to study how language models compress fluid trans-border ethnic identities into rigid nation-state categories. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, or efficient deployment.
Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities Proposes a diagnostic framework to study how language models compress fluid trans-border ethnic identities into rigid nation-state categories. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not investigate retrieval-augmented architectures or external knowledge grounding.

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