Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
SceneWeave: Audience-Guided Scene Exploration for Dramatic Adaptation Introduces a system that generates and compares alternative scene drafts to guide full dramatic script adaptations from prose. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on creative adaptation quality, not safety risks or guardrails.
SceneWeave: Audience-Guided Scene Exploration for Dramatic Adaptation Introduces a system that generates and compares alternative scene drafts to guide full dramatic script adaptations from prose. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not involve external database retrieval or grounding techniques.
SceneWeave: Audience-Guided Scene Exploration for Dramatic Adaptation Introduces a system that generates and compares alternative scene drafts to guide full dramatic script adaptations from prose. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient deployment.
SceneWeave: Audience-Guided Scene Exploration for Dramatic Adaptation Introduces a system that generates and compares alternative scene drafts to guide full dramatic script adaptations from prose. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Addresses dramatic scriptwriting rather than computer programming code.
SceneWeave: Audience-Guided Scene Exploration for Dramatic Adaptation Introduces a system that generates and compares alternative scene drafts to guide full dramatic script adaptations from prose. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Develops a creative writing system rather than an LLM evaluation benchmark.
Bias Mitigation in Audio-Enabled LLMs through Adversarial Training and Distillation Methods Applies adversarial training and distillation to remove unwanted gender bias in audio-enabled language models. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on audio speech bias rather than software code generation.
Bias Mitigation in Audio-Enabled LLMs through Adversarial Training and Distillation Methods Applies adversarial training and distillation to remove unwanted gender bias in audio-enabled language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not study external document retrieval or grounding methods.
Bias Mitigation in Audio-Enabled LLMs through Adversarial Training and Distillation Methods Applies adversarial training and distillation to remove unwanted gender bias in audio-enabled language models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Addresses audio model bias mitigation, not multi-step agent planning.
Bias Mitigation in Audio-Enabled LLMs through Adversarial Training and Distillation Methods Applies adversarial training and distillation to remove unwanted gender bias in audio-enabled language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on bias mitigation techniques rather than evaluation benchmarks.
Behavioral Forgetting Is Not Privacy in Machine Unlearning Demonstrates that machine unlearning often fails to prevent targeted information recovery across alternative multilingual access paths. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model compression, quantization, or inference speed.
Behavioral Forgetting Is Not Privacy in Machine Unlearning Demonstrates that machine unlearning often fails to prevent targeted information recovery across alternative multilingual access paths. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Evaluates multilingual text models without image, video, or audio data.
Behavioral Forgetting Is Not Privacy in Machine Unlearning Demonstrates that machine unlearning often fails to prevent targeted information recovery across alternative multilingual access paths. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Studies cross-lingual privacy unlearning, not code generation or analysis.
Behavioral Forgetting Is Not Privacy in Machine Unlearning Demonstrates that machine unlearning often fails to prevent targeted information recovery across alternative multilingual access paths. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Examines internal model memorization, not retrieval-augmented pipelines.
Behavioral Forgetting Is Not Privacy in Machine Unlearning Demonstrates that machine unlearning often fails to prevent targeted information recovery across alternative multilingual access paths. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on machine unlearning privacy, not autonomous agentic systems.
Content associations with answer selection and engagement in supply chain community question answering Analyzes how specific nouns and semantic clusters relate to answer selection and user engagement in logistics question-answering forums. Peer-reviewed	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Contains no work on model efficiency, distillation, or quantization.
Content associations with answer selection and engagement in supply chain community question answering Analyzes how specific nouns and semantic clusters relate to answer selection and user engagement in logistics question-answering forums. Peer-reviewed	AI agents via arxiv · 17 Oct, 06:17	Not relevant Analyzes human community forum posts, not autonomous AI agents.
Content associations with answer selection and engagement in supply chain community question answering Analyzes how specific nouns and semantic clusters relate to answer selection and user engagement in logistics question-answering forums. Peer-reviewed	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Studies logistics Q&A text rather than software programming or code.
Content associations with answer selection and engagement in supply chain community question answering Analyzes how specific nouns and semantic clusters relate to answer selection and user engagement in logistics question-answering forums. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Analyzes human forum dynamics rather than evaluating language models.
Content associations with answer selection and engagement in supply chain community question answering Analyzes how specific nouns and semantic clusters relate to answer selection and user engagement in logistics question-answering forums. Peer-reviewed	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Analyzes text forum data without multimodal vision or audio components.
Content associations with answer selection and engagement in supply chain community question answering Analyzes how specific nouns and semantic clusters relate to answer selection and user engagement in logistics question-answering forums. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not study retrieval-augmented generation systems or architecture.
Content associations with answer selection and engagement in supply chain community question answering Analyzes how specific nouns and semantic clusters relate to answer selection and user engagement in logistics question-answering forums. Peer-reviewed	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not investigate AI safety, guardrails, or alignment risks.
N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Models Proposes a compressed representation using Gaussian trellises to quantize conditional memory lookup tables down to two bits. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Deals with table quantization, not safety, robustness, or alignment.
N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Models Proposes a compressed representation using Gaussian trellises to quantize conditional memory lookup tables down to two bits. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Targets neural network embedding tables rather than software engineering tasks.
N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Models Proposes a compressed representation using Gaussian trellises to quantize conditional memory lookup tables down to two bits. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on model memory table compression, not agent planning.
N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Models Proposes a compressed representation using Gaussian trellises to quantize conditional memory lookup tables down to two bits. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on language model parameter quantization without multimodal processing.
N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Models Proposes a compressed representation using Gaussian trellises to quantize conditional memory lookup tables down to two bits. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Compreses internal model parameter tables, not external RAG document retrieval.
N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Models Proposes a compressed representation using Gaussian trellises to quantize conditional memory lookup tables down to two bits. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a compression method rather than benchmark evaluation frameworks.
Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios Introduces a non-autoregressive transformer model that dynamically decides tag generation order at inference time for low-resource sequence labeling. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses grammatical tagging rather than deployment safety or jailbreaks.
Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios Introduces a non-autoregressive transformer model that dynamically decides tag generation order at inference time for low-resource sequence labeling. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a decoding method rather than benchmarking or evaluation protocols.
Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios Introduces a non-autoregressive transformer model that dynamically decides tag generation order at inference time for low-resource sequence labeling. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses entirely on text sequence labeling without multimodal inputs.
Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios Introduces a non-autoregressive transformer model that dynamically decides tag generation order at inference time for low-resource sequence labeling. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Investigates decoding order for low-resource tagging, not model compression.
Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios Introduces a non-autoregressive transformer model that dynamically decides tag generation order at inference time for low-resource sequence labeling. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Deals with sequence labeling decoding, not autonomous multi-step agents.
Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios Introduces a non-autoregressive transformer model that dynamically decides tag generation order at inference time for low-resource sequence labeling. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on NLP part-of-speech and CCG tagging, not code synthesis.
Dynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios Introduces a non-autoregressive transformer model that dynamically decides tag generation order at inference time for low-resource sequence labeling. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not involve external retrieval or document augmentation.
TEM: Temporal State Tracking Memory for Large Language Model Agents Presents a training-free framework that tracks changing conversational facts as versioned records to improve agent memory retrieval. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on agent memory architectures rather than model quantization or distillation.
TEM: Temporal State Tracking Memory for Large Language Model Agents Presents a training-free framework that tracks changing conversational facts as versioned records to improve agent memory retrieval. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Operates entirely on conversational text without image or audio modalities.
TEM: Temporal State Tracking Memory for Large Language Model Agents Presents a training-free framework that tracks changing conversational facts as versioned records to improve agent memory retrieval. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a memory system rather than addressing LLM benchmarking methodology.
TEM: Temporal State Tracking Memory for Large Language Model Agents Presents a training-free framework that tracks changing conversational facts as versioned records to improve agent memory retrieval. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not focus on safety guardrails, jailbreaks, or unlearning.
TEM: Temporal State Tracking Memory for Large Language Model Agents Presents a training-free framework that tracks changing conversational facts as versioned records to improve agent memory retrieval. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on dialogue memory tracking, not code generation or review.
Learning Speech Attribute Representations via Multi-Teacher Distillation Distills multiple speech attributes from four teacher models into a single shared speech encoder. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents a representation distillation technique rather than a benchmarking framework.
Learning Speech Attribute Representations via Multi-Teacher Distillation Distills multiple speech attributes from four teacher models into a single shared speech encoder. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not examine retrieval-augmented generation or external document grounding.
Learning Speech Attribute Representations via Multi-Teacher Distillation Distills multiple speech attributes from four teacher models into a single shared speech encoder. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not study safety risks, jailbreaks, or deployment guardrails.
Learning Speech Attribute Representations via Multi-Teacher Distillation Distills multiple speech attributes from four teacher models into a single shared speech encoder. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies speech encoders and distillation, not tool-using agentic workflows.
Learning Speech Attribute Representations via Multi-Teacher Distillation Distills multiple speech attributes from four teacher models into a single shared speech encoder. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on speech processing rather than source code generation.
P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing Proposes a reinforcement learning credit-assignment method for document parsing models to align rewards with structured page elements. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Optimizes parsing accuracy rather than safety, red-teaming, or guardrails.
P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing Proposes a reinforcement learning credit-assignment method for document parsing models to align rewards with structured page elements. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Addresses document parsing rather than software programming or code generation.
P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing Proposes a reinforcement learning credit-assignment method for document parsing models to align rewards with structured page elements. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on document parsing policies, not autonomous planning agents.
P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing Proposes a reinforcement learning credit-assignment method for document parsing models to align rewards with structured page elements. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or inference speedup.
P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing Proposes a reinforcement learning credit-assignment method for document parsing models to align rewards with structured page elements. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Applies vision to document text, not industrial manufacturing or inspection.
P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing Proposes a reinforcement learning credit-assignment method for document parsing models to align rewards with structured page elements. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a training credit assignment method, not an evaluation framework.
P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing Proposes a reinforcement learning credit-assignment method for document parsing models to align rewards with structured page elements. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Deals with parsing layout structure, not retrieval-augmented generation pipelines.
WEAVE: Weaving Real-World Code Evolution into Dependency-Structured Tasks for Benchmarking and Training Code Agents Introduces a benchmark that structures multi-pull-request code changes into dependency graphs to evaluate long-horizon software engineering agents. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Examines code task progress rather than safety guardrails or vulnerabilities.
WEAVE: Weaving Real-World Code Evolution into Dependency-Structured Tasks for Benchmarking and Training Code Agents Introduces a benchmark that structures multi-pull-request code changes into dependency graphs to evaluate long-horizon software engineering agents. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model compression, pruning, quantization, or efficient serving.
WEAVE: Weaving Real-World Code Evolution into Dependency-Structured Tasks for Benchmarking and Training Code Agents Introduces a benchmark that structures multi-pull-request code changes into dependency graphs to evaluate long-horizon software engineering agents. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Only deals with text and code repositories, not multimodal data.
WEAVE: Weaving Real-World Code Evolution into Dependency-Structured Tasks for Benchmarking and Training Code Agents Introduces a benchmark that structures multi-pull-request code changes into dependency graphs to evaluate long-horizon software engineering agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on software engineering agents rather than retrieval-augmented generation systems.
Effectiveness of Hidden-State Probes for Localizing Reasoning Failures Evaluates whether internal hidden-state probes can identify failure modes and pinpoint failing steps in multi-step reasoning traces. Peer-reviewed	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Studies general mathematical and reasoning traces, not software engineering or code.
Effectiveness of Hidden-State Probes for Localizing Reasoning Failures Evaluates whether internal hidden-state probes can identify failure modes and pinpoint failing steps in multi-step reasoning traces. Peer-reviewed	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses solely on text-based language model reasoning without multimodal inputs.
Effectiveness of Hidden-State Probes for Localizing Reasoning Failures Evaluates whether internal hidden-state probes can identify failure modes and pinpoint failing steps in multi-step reasoning traces. Peer-reviewed	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Tests 4B and 8B models but not model compression or efficiency.
Effectiveness of Hidden-State Probes for Localizing Reasoning Failures Evaluates whether internal hidden-state probes can identify failure modes and pinpoint failing steps in multi-step reasoning traces. Peer-reviewed	AI agents via arxiv · 17 Oct, 06:17	Not relevant Analyzes hidden-state probes in reasoning traces, not multi-step tool-using agents.
Effectiveness of Hidden-State Probes for Localizing Reasoning Failures Evaluates whether internal hidden-state probes can identify failure modes and pinpoint failing steps in multi-step reasoning traces. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not involve external document retrieval, grounding, or RAG architectures.

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