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
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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates text-only summarization without any visual, audio, or multimodal components.
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	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.
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.
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.
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	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	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	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	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Pertains to human preference feedback rather than software engineering or code models.
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.
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.
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.
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 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	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	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	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	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.
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.
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 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.
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.
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.
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 agents via arxiv · 15 Oct, 15:06	Not relevant Concerns distributed pretraining optimizers rather than multi-step language model agents.
Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap Shows that incorporating static item value into dense retrieval outperforms generative retrieval in e-commerce recommendation settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on retrieval algorithms rather than language model evaluation frameworks or leaderboards.
Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap Shows that incorporating static item value into dense retrieval outperforms generative retrieval in e-commerce recommendation settings. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on candidate retrieval for recommendation rather than multi-step agent planning or tools.
Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap Shows that incorporating static item value into dense retrieval outperforms generative retrieval in e-commerce recommendation settings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with recommendation retrieval accuracy rather than AI safety or reliability.
Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap Shows that incorporating static item value into dense retrieval outperforms generative retrieval in e-commerce recommendation settings. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on retrieval quality comparison rather than model compression or quantization techniques.
Plan or Retrieve First? A Scale Study of Tool Discovery Architectures Compares retrieve-then-plan and plan-then-retrieve strategies for tool discovery across varying tool registry complexities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on tool retrieval efficiency rather than safety guardrails or jailbreak mitigation.
Plan or Retrieve First? A Scale Study of Tool Discovery Architectures Compares retrieve-then-plan and plan-then-retrieve strategies for tool discovery across varying tool registry complexities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent tool discovery architectures rather than general benchmark validity or evaluation.
Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization Introduces a prompt optimization framework that monitors item-level regressions and recoveries rather than relying solely on aggregate accuracy. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Analyzes prompt optimization across benchmarks rather than external knowledge retrieval or RAG.
Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization Introduces a prompt optimization framework that monitors item-level regressions and recoveries rather than relying solely on aggregate accuracy. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies prompt optimization across domains rather than code generation or software engineering tools.
Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization Introduces a prompt optimization framework that monitors item-level regressions and recoveries rather than relying solely on aggregate accuracy. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies automatic prompt optimization dynamics rather than multi-step autonomous tool-using agents.
Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization Introduces a prompt optimization framework that monitors item-level regressions and recoveries rather than relying solely on aggregate accuracy. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Investigates text-based prompt optimization without multimodal components.
Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization Introduces a prompt optimization framework that monitors item-level regressions and recoveries rather than relying solely on aggregate accuracy. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines prompt engineering metrics rather than model quantization, distillation, or serving.
DexMimic: A Generalist Dexterous Hand-Object Tracking Policy via Value-Guided Reset Allocation Introduces a generalist policy that tracks diverse dexterous hand-object interactions using value-guided reset allocation during training. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on dexterous hand manipulation rather than industrial visual inspection or defect detection.
DexMimic: A Generalist Dexterous Hand-Object Tracking Policy via Value-Guided Reset Allocation Introduces a generalist policy that tracks diverse dexterous hand-object interactions using value-guided reset allocation during training. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Covers physical robotic hand control rather than LLM-based planning or tool-use agents.
DexMimic: A Generalist Dexterous Hand-Object Tracking Policy via Value-Guided Reset Allocation Introduces a generalist policy that tracks diverse dexterous hand-object interactions using value-guided reset allocation during training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with motor control tracking rather than AI safety, hallucination, or alignment.
DexMimic: A Generalist Dexterous Hand-Object Tracking Policy via Value-Guided Reset Allocation Introduces a generalist policy that tracks diverse dexterous hand-object interactions using value-guided reset allocation during training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on reinforcement learning for robotics rather than testing language models.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Deals with on-screen component grounding rather than external knowledge retrieval.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a dataset and model for GUI grounding rather than evaluating LLM benchmark reliability.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses GUI referring expressions rather than safety guardrails, jailbreaking, or red teaming.

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