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
Towards the Roof of the World: NLP Progress in Tibetan Languages Surveys twenty-two years of Tibetan natural language processing literature across corpora, speech processing, and language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not study retrieval-augmented generation architectures.
Towards the Roof of the World: NLP Progress in Tibetan Languages Surveys twenty-two years of Tibetan natural language processing literature across corpora, speech processing, and language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not cover code models or software development tools.
Towards the Roof of the World: NLP Progress in Tibetan Languages Surveys twenty-two years of Tibetan natural language processing literature across corpora, speech processing, and language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a broad literature review across Tibetan NLP rather than LLM evaluation methods.
Towards the Roof of the World: NLP Progress in Tibetan Languages Surveys twenty-two years of Tibetan natural language processing literature across corpora, speech processing, and language models. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses broadly on NLP for Tibetan rather than multimodal models.
Towards the Roof of the World: NLP Progress in Tibetan Languages Surveys twenty-two years of Tibetan natural language processing literature across corpora, speech processing, and language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not focus on deployment safety, jailbreaks, or reliability.
Towards the Roof of the World: NLP Progress in Tibetan Languages Surveys twenty-two years of Tibetan natural language processing literature across corpora, speech processing, and language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant A bibliometric survey of low-resource NLP, not agentic planning or tools.
Towards the Roof of the World: NLP Progress in Tibetan Languages Surveys twenty-two years of Tibetan natural language processing literature across corpora, speech processing, and language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression or efficient inference methods.
Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR Investigates continual learning regularization techniques for speech recognition during domain adaptation on financial earnings calls. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies continual learning adaptation, not AI safety, red-teaming, or guardrails.
Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR Investigates continual learning regularization techniques for speech recognition during domain adaptation on financial earnings calls. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses continual learning in speech recognition rather than agent systems.
Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR Investigates continual learning regularization techniques for speech recognition during domain adaptation on financial earnings calls. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Investigates catastrophic forgetting regularization rather than efficiency, quantization, or distillation.
Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR Investigates continual learning regularization techniques for speech recognition during domain adaptation on financial earnings calls. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not involve code generation or software engineering.
Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR Investigates continual learning regularization techniques for speech recognition during domain adaptation on financial earnings calls. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses purely on speech recognition adaptation rather than multimodal LLMs.
Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR Investigates continual learning regularization techniques for speech recognition during domain adaptation on financial earnings calls. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not cover retrieval-augmented generation.
Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR Investigates continual learning regularization techniques for speech recognition during domain adaptation on financial earnings calls. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies continual learning regularization techniques rather than LLM benchmark evaluation.
Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalization Evaluates DistilHuBERT-based accent classification under speaker-disjoint and cross-corpus conditions to measure generalization to unseen speakers. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Uses DistilHuBERT as a test subject rather than proposing model efficiency methods.
Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalization Evaluates DistilHuBERT-based accent classification under speaker-disjoint and cross-corpus conditions to measure generalization to unseen speakers. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Not related to coding assistants or program repair.
Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalization Evaluates DistilHuBERT-based accent classification under speaker-disjoint and cross-corpus conditions to measure generalization to unseen speakers. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not involve multimodal language-vision models.
Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalization Evaluates DistilHuBERT-based accent classification under speaker-disjoint and cross-corpus conditions to measure generalization to unseen speakers. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address AI safety risks, hallucination, or prompt injection.
Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalization Evaluates DistilHuBERT-based accent classification under speaker-disjoint and cross-corpus conditions to measure generalization to unseen speakers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval-augmented generation.
Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalization Evaluates DistilHuBERT-based accent classification under speaker-disjoint and cross-corpus conditions to measure generalization to unseen speakers. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech accent classification rather than agentic planning or tool use.
Oído: Open-Vocabulary Speech Recognition on a $5 Microcontroller without a Neural Accelerator Implements open-vocabulary speech recognition on a low-cost ESP32-S3 microcontroller without dedicated neural accelerators. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve external document retrieval or RAG pipelines.
Oído: Open-Vocabulary Speech Recognition on a $5 Microcontroller without a Neural Accelerator Implements open-vocabulary speech recognition on a low-cost ESP32-S3 microcontroller without dedicated neural accelerators. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address AI safety, jailbreaks, or guardrails.
Oído: Open-Vocabulary Speech Recognition on a $5 Microcontroller without a Neural Accelerator Implements open-vocabulary speech recognition on a low-cost ESP32-S3 microcontroller without dedicated neural accelerators. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses exclusively on audio/speech recognition on microcontrollers, not multimodal LLMs.
Oído: Open-Vocabulary Speech Recognition on a $5 Microcontroller without a Neural Accelerator Implements open-vocabulary speech recognition on a low-cost ESP32-S3 microcontroller without dedicated neural accelerators. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an on-device inference system rather than LLM evaluation methodology.
Oído: Open-Vocabulary Speech Recognition on a $5 Microcontroller without a Neural Accelerator Implements open-vocabulary speech recognition on a low-cost ESP32-S3 microcontroller without dedicated neural accelerators. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on microcontroller speech recognition, not autonomous LLM agents.
Oído: Open-Vocabulary Speech Recognition on a $5 Microcontroller without a Neural Accelerator Implements open-vocabulary speech recognition on a low-cost ESP32-S3 microcontroller without dedicated neural accelerators. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not focus on software engineering or code generation models.
Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities Proposes a workflow to enrich design-time cyber-physical system models using empirical evidence collected from simulations and operational runs. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation architectures.
Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities Proposes a workflow to enrich design-time cyber-physical system models using empirical evidence collected from simulations and operational runs. Peer-reviewed	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on cyber-physical systems modeling rather than LLM-based coding tools.
Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities Proposes a workflow to enrich design-time cyber-physical system models using empirical evidence collected from simulations and operational runs. Peer-reviewed	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate multimodal language models.
Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities Proposes a workflow to enrich design-time cyber-physical system models using empirical evidence collected from simulations and operational runs. Peer-reviewed	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not involve model compression, small models, or efficient serving.
Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities Proposes a workflow to enrich design-time cyber-physical system models using empirical evidence collected from simulations and operational runs. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Pertains to physical systems design rather than LLM safety or deployment guardrails.
Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities Proposes a workflow to enrich design-time cyber-physical system models using empirical evidence collected from simulations and operational runs. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Pertains to system engineering simulation rather than LLM evaluation benchmarks.
Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities Proposes a workflow to enrich design-time cyber-physical system models using empirical evidence collected from simulations and operational runs. Peer-reviewed	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on cyber-physical systems design engineering, not LLM agents.
A Better Metric Will Not Save You: What You Compare Beats How You Compare It Analyzes representational similarity metrics and demonstrates that input category selection has a larger impact on comparisons than the choice of metric. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate safety, jailbreaks, or deployment reliability risks.
A Better Metric Will Not Save You: What You Compare Beats How You Compare It Analyzes representational similarity metrics and demonstrates that input category selection has a larger impact on comparisons than the choice of metric. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Investigates representational similarity metrics rather than model compression or efficiency.
A Better Metric Will Not Save You: What You Compare Beats How You Compare It Analyzes representational similarity metrics and demonstrates that input category selection has a larger impact on comparisons than the choice of metric. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on multimodal architectures or vision-language integration.
A Better Metric Will Not Save You: What You Compare Beats How You Compare It Analyzes representational similarity metrics and demonstrates that input category selection has a larger impact on comparisons than the choice of metric. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not study retrieval-augmented generation or search systems.
A Better Metric Will Not Save You: What You Compare Beats How You Compare It Analyzes representational similarity metrics and demonstrates that input category selection has a larger impact on comparisons than the choice of metric. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates representation similarity metrics rather than agent planning or workflows.
A Better Metric Will Not Save You: What You Compare Beats How You Compare It Analyzes representational similarity metrics and demonstrates that input category selection has a larger impact on comparisons than the choice of metric. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not address code models or software engineering tasks.
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not focus on coding assistants, code generation, or software engineering.
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses orchestration policy learning, not model compression or small-model serving.
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not address multimodal or vision-language models.
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes an orchestration method, not a novel evaluation methodology or benchmark.
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on agentic orchestration rather than document retrieval or grounding.
Proactive Agentic Orchestration Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent task orchestration, not safety, guardrails, or alignment.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies internal spatial representations without retrieval-augmented generation.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Investigates mechanistic spatial representations rather than proposing new benchmark suites.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Analyzes representation subspaces in MLLMs rather than autonomous agent systems.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on internal representations of spatial reasoning rather than safety risks or guardrails.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Analyzes internal representations of MLLMs rather than industrial vision inspection or robotics perception.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Investigates spatial reasoning in multimodal models, unrelated to software engineering or code.
S-Space: Exploring Spatial Workspace in Multimodal Language Models The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Explores internal representation geometry without focusing on model compression or serving efficiency.
Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear? The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Evaluates speech models without addressing small models, distillation, or compression.
Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear? The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies paralinguistic personalization and bias rather than autonomous agent systems.
Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear? The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Examines speech LLM bias and personalization without retrieval-augmented generation.
Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear? The paper introduces a benchmark to measure demographic robustness, stereotyping, and intentional personalization in speech language models. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on speech LLMs and paralinguistics, unrelated to code or software development.
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Evaluates patch runtime performance in SWE benchmarks rather than agent planning architectures.
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Analyzes code execution runtime efficiency rather than safety, guardrails, or alignment.
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Measures runtime performance of generated patches, not language model compression or serving.
Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution The paper analyzes the runtime performance differences of test-passing issue resolution patches generated by language models on SWE-bench. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Studies software engineering code patches without multimodal vision or audio components.

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