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
GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning Presents a large graphical user interface grounding dataset and a combined distillation and reinforcement learning training method for agents. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Pertains to computer screen GUIs rather than manufacturing or industrial defect inspection.
GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning Presents a large graphical user interface grounding dataset and a combined distillation and reinforcement learning training method for agents. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on user interface grounding rather than source code generation or software engineering.
GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning Presents a large graphical user interface grounding dataset and a combined distillation and reinforcement learning training method for agents. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on grounding accuracy rather than model compression or efficient serving architectures.
Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Covers generic spatial reasoning rather than industrial inspection or manufacturing defect detection.
Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines spatial perception datasets rather than AI safety, red-teaming, or guardrails.
Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Surveys spatial vision datasets without relevance to software code or program repair.
Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Surveys spatial dataset construction for vision models rather than autonomous agent systems.
Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Surveys datasets and evaluation rather than model pruning, quantization, or compression.
Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address external document retrieval or retrieval-augmented generation pipelines.
From Copying to Composition: How Language Models Develop In-Context Learning Analyzes how language models develop in-context learning by separating simple demonstration copying from multi-token rule composition across training checkpoints. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies prompt-level in-context learning rather than external retrieval systems or RAG.
From Copying to Composition: How Language Models Develop In-Context Learning Analyzes how language models develop in-context learning by separating simple demonstration copying from multi-token rule composition across training checkpoints. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on context learning dynamics rather than model compression or inference efficiency.
From Copying to Composition: How Language Models Develop In-Context Learning Analyzes how language models develop in-context learning by separating simple demonstration copying from multi-token rule composition across training checkpoints. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates foundational in-context learning mechanics rather than agentic planning or tools.
From Copying to Composition: How Language Models Develop In-Context Learning Analyzes how language models develop in-context learning by separating simple demonstration copying from multi-token rule composition across training checkpoints. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Explores mechanistic in-context learning rather than deployment safety, red teaming, or guardrails.
From Copying to Composition: How Language Models Develop In-Context Learning Analyzes how language models develop in-context learning by separating simple demonstration copying from multi-token rule composition across training checkpoints. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Investigates unimodal text language models without multimodal components.
From Copying to Composition: How Language Models Develop In-Context Learning Analyzes how language models develop in-context learning by separating simple demonstration copying from multi-token rule composition across training checkpoints. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Examines general prompt comprehension rather than software engineering or code generation.
Genesis Steering: Better Activation Steering Through Training Data Steers language model activations using vectors derived directly from the model's own training data or inferred proxy data. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates activation steering vectors rather than multi-step agent planning or tool use.
Genesis Steering: Better Activation Steering Through Training Data Steers language model activations using vectors derived directly from the model's own training data or inferred proxy data. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on representation steering rather than model compression, quantization, or efficient serving.
Genesis Steering: Better Activation Steering Through Training Data Steers language model activations using vectors derived directly from the model's own training data or inferred proxy data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops an activation steering technique rather than an evaluation framework or benchmark.
Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming The paper proposes a training-free structured pruning method that trims multilayer perceptron layers while preserving attention mechanisms for reasoning tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external document retrieval, grounding, or RAG pipelines.
Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming The paper proposes a training-free structured pruning method that trims multilayer perceptron layers while preserving attention mechanisms for reasoning tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a pruning method rather than a novel benchmark or evaluation technique.
Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming The paper proposes a training-free structured pruning method that trims multilayer perceptron layers while preserving attention mechanisms for reasoning tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on MLP pruning for transformer layers, not multi-step agent architectures.
Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming The paper proposes a training-free structured pruning method that trims multilayer perceptron layers while preserving attention mechanisms for reasoning tasks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Exclusively addresses text-based reasoning models without multimodal data.
Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming The paper proposes a training-free structured pruning method that trims multilayer perceptron layers while preserving attention mechanisms for reasoning tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates model pruning and efficiency rather than deployment safety or jailbreak prevention.
Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming The paper proposes a training-free structured pruning method that trims multilayer perceptron layers while preserving attention mechanisms for reasoning tasks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses general mathematical reasoning rather than software engineering or code generation.
Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft The paper demonstrates onboard thermal segmentation training and closed-loop attitude control on a satellite in low Earth orbit. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Applies to spacecraft attitude control rather than manufacturing inspection or factory defect detection.
Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft The paper demonstrates onboard thermal segmentation training and closed-loop attitude control on a satellite in low Earth orbit. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Demonstrates an onboard robotics deployment rather than proposing LLM evaluation frameworks.
Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft The paper demonstrates onboard thermal segmentation training and closed-loop attitude control on a satellite in low Earth orbit. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Involves satellite attitude control and onboard segmentation, not LLM agents or tool planning.
Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft The paper demonstrates onboard thermal segmentation training and closed-loop attitude control on a satellite in low Earth orbit. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with embedded spacecraft fault tolerance instead of LLM safety or alignment.
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization The paper introduces a transformer framework trained on matched molecular pairs for prompt-guided, multi-property molecular optimization in low-data regimes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Concerns biochemical property optimization rather than AI safety or system guardrails.
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization The paper introduces a transformer framework trained on matched molecular pairs for prompt-guided, multi-property molecular optimization in low-data regimes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, efficient serving, or small language models.
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization The paper introduces a transformer framework trained on matched molecular pairs for prompt-guided, multi-property molecular optimization in low-data regimes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on domain-specific molecule optimization rather than general LLM evaluation methodology.
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization The paper introduces a transformer framework trained on matched molecular pairs for prompt-guided, multi-property molecular optimization in low-data regimes. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on molecular SMILES generation rather than multi-step agent planning or tool execution.
Imagining Goals with Geometric Horizon Models for Policy Generalization The paper proposes sampling from a geometric horizon world model to imagine novel outcomes for zero-shot policy generalization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not study safety, guardrails, hallucinations, or deployment reliability.
Imagining Goals with Geometric Horizon Models for Policy Generalization The paper proposes sampling from a geometric horizon world model to imagine novel outcomes for zero-shot policy generalization. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on reinforcement learning policy generalization, not LLM planning or tool use.
Imagining Goals with Geometric Horizon Models for Policy Generalization The paper proposes sampling from a geometric horizon world model to imagine novel outcomes for zero-shot policy generalization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates RL policy generalization on existing benchmarks rather than proposing LLM benchmarks.
Imagining Goals with Geometric Horizon Models for Policy Generalization The paper proposes sampling from a geometric horizon world model to imagine novel outcomes for zero-shot policy generalization. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Not about model compression, quantization, distillation, or efficient inference.
Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders The paper presents a system using technical language processing to transform industrial maintenance work order records into an ontology. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Processes industrial free-text maintenance orders without multimodal image or audio modalities.
Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders The paper presents a system using technical language processing to transform industrial maintenance work order records into an ontology. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address small language models, quantization, pruning, or serving efficiency.
Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders The paper presents a system using technical language processing to transform industrial maintenance work order records into an ontology. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on knowledge extraction and verification rather than AI safety or guardrails.
Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders The paper presents a system using technical language processing to transform industrial maintenance work order records into an ontology. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Describes ontology construction from maintenance logs rather than autonomous planning agents.
Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders The paper presents a system using technical language processing to transform industrial maintenance work order records into an ontology. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies NLP to industrial maintenance logs rather than code generation or repair.
Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders The paper presents a system using technical language processing to transform industrial maintenance work order records into an ontology. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on ontology building rather than general LLM evaluation benchmarks or methods.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies existing speech leaderboards rather than proposing LLM benchmarks or evaluation methods.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on speech recognition model distillation rather than agent planning or tool use.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation or external document grounding.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses ASR distillation trade-offs rather than safety, alignment, or guardrail systems.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Concerns speech recognition, not software engineering or code generation.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses purely on automatic speech recognition distillation, not multimodal cross-modal reasoning.
Graph-Based Orchestration of Heterogeneous AI Models: A Control Node Approach to Composite Intelligence The paper presents a graph-based framework using control nodes and behavior trees to coordinate heterogeneous artificial intelligence models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on system composition and execution graphs rather than safety or reliability guardrails.
Graph-Based Orchestration of Heterogeneous AI Models: A Control Node Approach to Composite Intelligence The paper presents a graph-based framework using control nodes and behavior trees to coordinate heterogeneous artificial intelligence models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a system orchestration architecture rather than an evaluation benchmark or methodology.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Tests operational synthesis from provided document packets rather than external retrieval systems.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates long-form planning determinations without multi-step autonomous tool use or execution.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on operational planning from text packets, not code generation or software repair.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Tests planning capability rather than deployment safety, guardrails, or alignment risks.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Benchmark study that does not address model compression, pruning, or efficient inference.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Exclusively uses textual operational document scenarios without multimodal inputs.
Compiling Expert Procedures into Verified Programs: When a Small Model Is Enough The paper compiles teacher model reasoning traces into verified programs that guide small models through complex procedural tool-use tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Aims to boost tool-use capability rather than studying safety risks, jailbreaks, or guardrails.
Compiling Expert Procedures into Verified Programs: When a Small Model Is Enough The paper compiles teacher model reasoning traces into verified programs that guide small models through complex procedural tool-use tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on a procedural execution system rather than benchmarking methodologies.
Into the Unknown: Benchmarking AI agents on open scientific research problems The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmark paper examining research capabilities rather than deployment safety or jailbreak prevention.
Into the Unknown: Benchmarking AI agents on open scientific research problems The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on multimodal reasoning across vision, audio, or document models.

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