Every paper checked — nothing hidden

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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
Manacá-1B-Instruct-2: Reproducible Safety Alignment for a Native Brazilian-Portuguese LLM. Negative DPO Results, a Controlled Composition Scan, and a Silent Failure Mode of the LLM Judge Documents safety alignment methods, negative preference tuning outcomes, and automated judge limitations for a Portuguese language model. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Aligns a general-purpose language model, not a coding-specific LLM.
Manacá-1B-Instruct-2: Reproducible Safety Alignment for a Native Brazilian-Portuguese LLM. Negative DPO Results, a Controlled Composition Scan, and a Silent Failure Mode of the LLM Judge Documents safety alignment methods, negative preference tuning outcomes, and automated judge limitations for a Portuguese language model. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses strictly on Portuguese text alignment, with no multimodal components.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Presents an inference efficiency technique, not an AI safety or robustness method.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text long-context KV cache compression, not multimodal models.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes KV-cache compression via test-time training, not autonomous agents.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a compression technique evaluated on existing benchmarks, not an evaluation benchmark.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses long-context attention efficiency rather than code generation or software engineering.
Latent Compaction with Test-Time Training Compresses long-context key-value caches using test-time training on generic document prompts without task-specific labels. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on long-context KV cache compaction rather than external document retrieval systems.
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical radiotherapy registration rather than AI deployment safety or jailbreaks.
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical radiotherapy image registration, not industrial or manufacturing defect inspection.
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Deals with medical deformable image registration, not autonomous LLM agents.
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a medical imaging technique rather than a language model benchmark.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with automated scientific research orchestration rather than AI safety or guardrails.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent orchestration rather than efficient model architectures or compression.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on orchestrating coding agents rather than document retrieval or RAG architectures.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Describes an agentic scientific system rather than an LLM evaluation benchmark.
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on coding and scientific orchestration, not multimodal vision-language models.
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench Introduces a multi-agent framework where simulated scientists formulate, test, and revise scientific conjectures alongside mathematical equations. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific discovery frameworks rather than model compression or efficiency.
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench Introduces a multi-agent framework where simulated scientists formulate, test, and revise scientific conjectures alongside mathematical equations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific discovery and equation benchmarking rather than AI safety.
Non-Abelian quantum Hall states at strong Landau level mixing: finding anyons with first-principles AI Uses a neural-network variational wave function to model non-Abelian quantum Hall states with realistic Landau level mixing. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Applies neural wave functions to quantum physics, not language model agents.
Non-Abelian quantum Hall states at strong Landau level mixing: finding anyons with first-principles AI Uses a neural-network variational wave function to model non-Abelian quantum Hall states with realistic Landau level mixing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses theoretical physics rather than AI safety, robustness, or reliability.
Non-Abelian quantum Hall states at strong Landau level mixing: finding anyons with first-principles AI Uses a neural-network variational wave function to model non-Abelian quantum Hall states with realistic Landau level mixing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on quantum Hall physics rather than language model evaluation benchmarks.
Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models Proposes an optimization method to discover undisclosed or harmful generation concepts embedded within diffusion model adaptation weights. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies LoRA concept discovery in diffusion models, not autonomous agents.
Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models Proposes an optimization method to discover undisclosed or harmful generation concepts embedded within diffusion model adaptation weights. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an optimization method to inspect adapters rather than an evaluation benchmark.
Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models Proposes an optimization method to discover undisclosed or harmful generation concepts embedded within diffusion model adaptation weights. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines concept discovery in LoRAs rather than model compression or inference efficiency.
Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali Evaluates closed and open models on English and Bengali abstractive summarization using lexical metrics, factuality checks, and automated judges. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Evaluates standard LLMs without proposing efficiency, quantization, or distillation techniques.
Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali Evaluates closed and open models on English and Bengali abstractive summarization using lexical metrics, factuality checks, and automated judges. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates general language models on Bengali and English text, not code.
Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali Evaluates closed and open models on English and Bengali abstractive summarization using lexical metrics, factuality checks, and automated judges. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on multilingual summarization evaluation, not autonomous multi-step planning or tools.
Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali Evaluates closed and open models on English and Bengali abstractive summarization using lexical metrics, factuality checks, and automated judges. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Evaluates abstractive summarization metrics, not retrieval-augmented generation or grounding mechanisms.
Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali Evaluates closed and open models on English and Bengali abstractive summarization using lexical metrics, factuality checks, and automated judges. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on multilingual summarization accuracy rather than safety risks or guardrails.
Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali Evaluates closed and open models on English and Bengali abstractive summarization using lexical metrics, factuality checks, and automated judges. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates text-only summarization without any visual, audio, or multimodal components.
Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction Improves indoor radio-map prediction by pretraining models on diverse synthetic floorplans and an open-source ray tracer. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates synthetic radio pathloss modeling rather than language model benchmarks.
Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction Improves indoor radio-map prediction by pretraining models on diverse synthetic floorplans and an open-source ray tracer. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on physical wireless radio propagation rather than LLM agents.
Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction Improves indoor radio-map prediction by pretraining models on diverse synthetic floorplans and an open-source ray tracer. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies synthetic pretraining for radio maps rather than efficient model compression or serving.
Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction Improves indoor radio-map prediction by pretraining models on diverse synthetic floorplans and an open-source ray tracer. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses wireless communications simulation rather than deployment safety or jailbreak prevention.
Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment Introduces a welfare-aware objective to allocate limited human feedback across diverse individuals when training pluralistic alignment models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses alignment data collection rather than autonomous agent planning or tool execution.
Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment Introduces a welfare-aware objective to allocate limited human feedback across diverse individuals when training pluralistic alignment models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text alignment preferences without multimodal data.
Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment Introduces a welfare-aware objective to allocate limited human feedback across diverse individuals when training pluralistic alignment models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external document retrieval, indexing, or RAG architectures.
Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment Introduces a welfare-aware objective to allocate limited human feedback across diverse individuals when training pluralistic alignment models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient inference.
Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment Introduces a welfare-aware objective to allocate limited human feedback across diverse individuals when training pluralistic alignment models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an alignment data acquisition framework rather than an evaluation benchmark.
Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment Introduces a welfare-aware objective to allocate limited human feedback across diverse individuals when training pluralistic alignment models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Pertains to human preference feedback rather than software engineering or code models.
Loss Curvature as a Sampling-Free Proxy for Memorization in Diffusion Models Proposes input loss curvature as a sampling-free metric to detect the onset of training data memorization in diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates diffusion memorization metrics rather than language model benchmarking.
Loss Curvature as a Sampling-Free Proxy for Memorization in Diffusion Models Proposes input loss curvature as a sampling-free metric to detect the onset of training data memorization in diffusion models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies diffusion model memorization rather than autonomous planning agents.
Loss Curvature as a Sampling-Free Proxy for Memorization in Diffusion Models Proposes input loss curvature as a sampling-free metric to detect the onset of training data memorization in diffusion models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on memorization dynamics rather than model compression, distillation, or serving.
Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT Combines spectral graph coarsening with continuous relaxation to find high-quality solutions for compute-constrained MAX-CUT problems. Peer-reviewed	AI agents via arxiv · 15 Oct, 15:06	Not relevant Concerns mathematical graph optimization rather than autonomous language model agents.
Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT Combines spectral graph coarsening with continuous relaxation to find high-quality solutions for compute-constrained MAX-CUT problems. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on combinatorial graph optimization rather than code generation or program repair.
Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT Combines spectral graph coarsening with continuous relaxation to find high-quality solutions for compute-constrained MAX-CUT problems. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates a mathematical solver rather than language model evaluation frameworks.
Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT Combines spectral graph coarsening with continuous relaxation to find high-quality solutions for compute-constrained MAX-CUT problems. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not involve multimodal language or vision models.
Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT Combines spectral graph coarsening with continuous relaxation to find high-quality solutions for compute-constrained MAX-CUT problems. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with combinatorial optimization algorithms rather than small language models or quantization.
Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT Combines spectral graph coarsening with continuous relaxation to find high-quality solutions for compute-constrained MAX-CUT problems. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Solves MAX-CUT optimization rather than retrieval-augmented generation or document search.
Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT Combines spectral graph coarsening with continuous relaxation to find high-quality solutions for compute-constrained MAX-CUT problems. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Concerns classical graph optimization rather than AI safety or deployment robustness.
Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams Argues that inferential accuracy fails to evaluate agent coordination safely because correlated errors can compound into costly team actions. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Deals with multi-agent coordination rather than software engineering or code generation.
Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams Argues that inferential accuracy fails to evaluate agent coordination safely because correlated errors can compound into costly team actions. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Investigates agent coordination without focusing on multimodal vision or audio processing.
Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams Argues that inferential accuracy fails to evaluate agent coordination safely because correlated errors can compound into costly team actions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not investigate model pruning, quantization, or efficient deployment.
Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams Argues that inferential accuracy fails to evaluate agent coordination safely because correlated errors can compound into costly team actions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address external information retrieval or grounded document systems.
RLPE: Teaching LLMs to Prompt with Reinforcement Learning Uses reinforcement learning to train an auxiliary model that inspects execution traces and generates targeted prompts for another system. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on prompt generation performance rather than AI safety, red teaming, or guardrails.
RLPE: Teaching LLMs to Prompt with Reinforcement Learning Uses reinforcement learning to train an auxiliary model that inspects execution traces and generates targeted prompts for another system. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Trains a model to generate prompts rather than designing multi-step autonomous planning agents.
RLPE: Teaching LLMs to Prompt with Reinforcement Learning Uses reinforcement learning to train an auxiliary model that inspects execution traces and generates targeted prompts for another system. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a prompt-learning method rather than evaluating benchmark integrity or LLM-as-a-judge.
How Inner Optimizers Shape Replica Scaling Studies how the choice of local optimizers affects model performance degradation when scaling distributed training replicas under fixed budgets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies optimizer scaling dynamics during training rather than post-training model evaluation.
How Inner Optimizers Shape Replica Scaling Studies how the choice of local optimizers affects model performance degradation when scaling distributed training replicas under fixed budgets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines optimization math for training rather than deployment safety, alignment, or guardrails.

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