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
HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation The paper proposes a method combining offline execution-verified prompt updates and test-time refinement to improve program generation in language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on code refinement optimization rather than benchmark design.
How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking The paper investigates base language model benchmarking setups to identify evaluation configurations that produce consistent, non-random, and monotonic performance signals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on benchmark setup reliability rather than safety risks.
How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking The paper investigates base language model benchmarking setups to identify evaluation configurations that produce consistent, non-random, and monotonic performance signals. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation.
How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking The paper investigates base language model benchmarking setups to identify evaluation configurations that produce consistent, non-random, and monotonic performance signals. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on base language model evaluation rather than code.
How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking The paper investigates base language model benchmarking setups to identify evaluation configurations that produce consistent, non-random, and monotonic performance signals. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Analyzes benchmarking methodology rather than autonomous agent systems.
How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking The paper investigates base language model benchmarking setups to identify evaluation configurations that produce consistent, non-random, and monotonic performance signals. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Analyzes benchmarking setups rather than model compression techniques.
How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking The paper investigates base language model benchmarking setups to identify evaluation configurations that produce consistent, non-random, and monotonic performance signals. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates language models across text tasks without multimodal data.
NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on sentiment analysis, not software development code.
NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not study safety risks, jailbreaks, or alignment.
NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals strictly with text sentiment analysis without multimodal data.
NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model pruning, distillation, or efficient serving.
NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents a sentiment benchmark, not multi-step agent planning.
NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not focus on retrieval-augmented generation systems.
Weighted Ensemble Sampling for Rare-Event Estimation in Language Models The paper introduces a training-free sampling framework that uses internal model activations to efficiently estimate rare failure probabilities in language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with language model text generation without multimodal inputs.
Weighted Ensemble Sampling for Rare-Event Estimation in Language Models The paper introduces a training-free sampling framework that uses internal model activations to efficiently estimate rare failure probabilities in language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external retrieval or document augmentation.
Weighted Ensemble Sampling for Rare-Event Estimation in Language Models The paper introduces a training-free sampling framework that uses internal model activations to efficiently estimate rare failure probabilities in language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on statistical sampling estimation rather than LLM agents.
Weighted Ensemble Sampling for Rare-Event Estimation in Language Models The paper introduces a training-free sampling framework that uses internal model activations to efficiently estimate rare failure probabilities in language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an estimation algorithm rather than a benchmark suite.
Weighted Ensemble Sampling for Rare-Event Estimation in Language Models The paper introduces a training-free sampling framework that uses internal model activations to efficiently estimate rare failure probabilities in language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate or generate programming code.
Weighted Ensemble Sampling for Rare-Event Estimation in Language Models The paper introduces a training-free sampling framework that uses internal model activations to efficiently estimate rare failure probabilities in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on rare-event sampling efficiency, not model compression.
DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression The paper proposes a parameter-efficient method leveraging frozen model representations for multi-output text regression tasks across diverse benchmarks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation pipelines.
DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression The paper proposes a parameter-efficient method leveraging frozen model representations for multi-output text regression tasks across diverse benchmarks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses strictly on text regression rather than multimodal tasks.
DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression The paper proposes a parameter-efficient method leveraging frozen model representations for multi-output text regression tasks across diverse benchmarks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a parameter-efficient model head rather than evaluation benchmarking.
DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression The paper proposes a parameter-efficient method leveraging frozen model representations for multi-output text regression tasks across diverse benchmarks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on multi-output text regression, not code generation.
DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression The paper proposes a parameter-efficient method leveraging frozen model representations for multi-output text regression tasks across diverse benchmarks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes a regression head architecture, not agentic planning.
DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression The paper proposes a parameter-efficient method leveraging frozen model representations for multi-output text regression tasks across diverse benchmarks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not focus on AI safety, red teaming, or guardrails.
From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring The paper presents a framework to formalize compute, labor, and workflow costs as core measurement design criteria for automated essay scoring. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines deployment economics rather than model compression techniques.
From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring The paper presents a framework to formalize compute, labor, and workflow costs as core measurement design criteria for automated essay scoring. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text essay scoring rather than multimodal models.
From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring The paper presents a framework to formalize compute, labor, and workflow costs as core measurement design criteria for automated essay scoring. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on essay scoring, not software engineering code.
From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring The paper presents a framework to formalize compute, labor, and workflow costs as core measurement design criteria for automated essay scoring. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on automated essay scoring frameworks, not agent planning.
From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring The paper presents a framework to formalize compute, labor, and workflow costs as core measurement design criteria for automated essay scoring. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address prompt injection, jailbreaks, or safety guardrails.
From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring The paper presents a framework to formalize compute, labor, and workflow costs as core measurement design criteria for automated essay scoring. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation architectures.
Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance The paper evaluates the ability of language model agents to diagnose appliance faults using a simulated appliance benchmark based on sensor data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on operational diagnostic benchmarking rather than AI safety.
Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance The paper evaluates the ability of language model agents to diagnose appliance faults using a simulated appliance benchmark based on sensor data. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on sensor telemetry in text format without multimodal vision.
Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance The paper evaluates the ability of language model agents to diagnose appliance faults using a simulated appliance benchmark based on sensor data. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on industrial appliance fault diagnostics rather than code.
Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance The paper evaluates the ability of language model agents to diagnose appliance faults using a simulated appliance benchmark based on sensor data. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, quantization, or efficient inference.
Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance The paper evaluates the ability of language model agents to diagnose appliance faults using a simulated appliance benchmark based on sensor data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate document retrieval or external knowledge bases.
Qwenfant: Train Big Character-level BabyLMs on Concise Parallel Text The paper presents an approach using character-level tokens, larger models, and synthetic data for the multilingual BabyLM data-efficiency challenge. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on multilingual text modeling, not code tasks.
Qwenfant: Train Big Character-level BabyLMs on Concise Parallel Text The paper presents an approach using character-level tokens, larger models, and synthetic data for the multilingual BabyLM data-efficiency challenge. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety risks, robustness, or guardrails.
Qwenfant: Train Big Character-level BabyLMs on Concise Parallel Text The paper presents an approach using character-level tokens, larger models, and synthetic data for the multilingual BabyLM data-efficiency challenge. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Submits a model to BabyLM rather than evaluating benchmark methodologies.
Qwenfant: Train Big Character-level BabyLMs on Concise Parallel Text The paper presents an approach using character-level tokens, larger models, and synthetic data for the multilingual BabyLM data-efficiency challenge. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies pre-training on synthetic data, not RAG systems.
Qwenfant: Train Big Character-level BabyLMs on Concise Parallel Text The paper presents an approach using character-level tokens, larger models, and synthetic data for the multilingual BabyLM data-efficiency challenge. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals strictly with text data rather than multimodal capabilities.
Qwenfant: Train Big Character-level BabyLMs on Concise Parallel Text The paper presents an approach using character-level tokens, larger models, and synthetic data for the multilingual BabyLM data-efficiency challenge. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on training compact language models, not agents.
PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information? The paper introduces a benchmark to evaluate whether language models can accurately detect appropriate and inappropriate uses of persona information in dialogue. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on dialogue personalization evaluation rather than RAG.
PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information? The paper introduces a benchmark to evaluate whether language models can accurately detect appropriate and inappropriate uses of persona information in dialogue. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text dialogue history rather than multimodal understanding.
PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information? The paper introduces a benchmark to evaluate whether language models can accurately detect appropriate and inappropriate uses of persona information in dialogue. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on benchmark evaluation of personalization rather than safety.
PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information? The paper introduces a benchmark to evaluate whether language models can accurately detect appropriate and inappropriate uses of persona information in dialogue. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates LLM evaluators on personalization errors, not agent planning.
PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information? The paper introduces a benchmark to evaluate whether language models can accurately detect appropriate and inappropriate uses of persona information in dialogue. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate or generate software code.
PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information? The paper introduces a benchmark to evaluate whether language models can accurately detect appropriate and inappropriate uses of persona information in dialogue. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address small models, distillation, or quantization.
Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows The paper formulates task routing across language model providers as an auction mechanism to encourage truthful cost reporting while minimizing context switching. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses economic mechanism design, not AI safety or jailbreaks.
Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows The paper formulates task routing across language model providers as an auction mechanism to encourage truthful cost reporting while minimizing context switching. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on provider routing mechanisms rather than RAG pipelines.
Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows The paper formulates task routing across language model providers as an auction mechanism to encourage truthful cost reporting while minimizing context switching. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies routing economics rather than model compression or efficient serving.
Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows The paper formulates task routing across language model providers as an auction mechanism to encourage truthful cost reporting while minimizing context switching. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on multi-provider agent routing without multimodal modalities.
Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows The paper formulates task routing across language model providers as an auction mechanism to encourage truthful cost reporting while minimizing context switching. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses procurement auctions in agent workflows, not code generation.
Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows The paper formulates task routing across language model providers as an auction mechanism to encourage truthful cost reporting while minimizing context switching. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on workflow routing algorithms rather than benchmark design.
TopicMerge: A Divide and Conquer Approach to Topic Modeling The paper introduces a multi-stage topic modeling pipeline that merges fine-grained document clusters using a language model to balance efficiency and quality. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaks, or hallucination mitigation.
TopicMerge: A Divide and Conquer Approach to Topic Modeling The paper introduces a multi-stage topic modeling pipeline that merges fine-grained document clusters using a language model to balance efficiency and quality. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with text document clustering without multimodal data.
TopicMerge: A Divide and Conquer Approach to Topic Modeling The paper introduces a multi-stage topic modeling pipeline that merges fine-grained document clusters using a language model to balance efficiency and quality. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Proposes a topic modeling approach rather than RAG systems.
TopicMerge: A Divide and Conquer Approach to Topic Modeling The paper introduces a multi-stage topic modeling pipeline that merges fine-grained document clusters using a language model to balance efficiency and quality. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, quantization, or distillation.
TopicMerge: A Divide and Conquer Approach to Topic Modeling The paper introduces a multi-stage topic modeling pipeline that merges fine-grained document clusters using a language model to balance efficiency and quality. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on document topic modeling rather than program generation.
TopicMerge: A Divide and Conquer Approach to Topic Modeling The paper introduces a multi-stage topic modeling pipeline that merges fine-grained document clusters using a language model to balance efficiency and quality. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a clustering pipeline rather than evaluation methodologies.

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