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
Beyond Model Bias: Cross-Entropy Based Rankings in Literary and Academic Texts Remain Stable across Small Language Models This paper studies cross-entropy surprisal metrics, showing that predictability rankings of texts remain consistent across multiple small language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval mechanisms or grounding.
Beyond Model Bias: Cross-Entropy Based Rankings in Literary and Academic Texts Remain Stable across Small Language Models This paper studies cross-entropy surprisal metrics, showing that predictability rankings of texts remain consistent across multiple small language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates cross-entropy predictability metrics, not agent workflows.
AD4EDU: Benchmarking University Lecture Audio Description across Accessibility Standards, Metrics, and Learner Preferences This paper introduces a benchmark and rule-based metric for generating and evaluating audio descriptions in university slide lectures. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model efficiency, distillation, or edge deployment.
AD4EDU: Benchmarking University Lecture Audio Description across Accessibility Standards, Metrics, and Learner Preferences This paper introduces a benchmark and rule-based metric for generating and evaluating audio descriptions in university slide lectures. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies audio descriptions for lecture slides, not autonomous agentic systems.
AD4EDU: Benchmarking University Lecture Audio Description across Accessibility Standards, Metrics, and Learner Preferences This paper introduces a benchmark and rule-based metric for generating and evaluating audio descriptions in university slide lectures. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on lecture accessibility descriptions, not programming or code generation.
AD4EDU: Benchmarking University Lecture Audio Description across Accessibility Standards, Metrics, and Learner Preferences This paper introduces a benchmark and rule-based metric for generating and evaluating audio descriptions in university slide lectures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on educational accessibility benchmarks rather than safety or red-teaming.
AD4EDU: Benchmarking University Lecture Audio Description across Accessibility Standards, Metrics, and Learner Preferences This paper introduces a benchmark and rule-based metric for generating and evaluating audio descriptions in university slide lectures. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation techniques.
UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison This paper evaluates encoder fine-tuning and synthetic data augmentation for fallacy detection and argument classification on Reddit discussions. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on text classification shared tasks, not agent architectures.
UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison This paper evaluates encoder fine-tuning and synthetic data augmentation for fallacy detection and argument classification on Reddit discussions. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals solely with text classification of Reddit discussions.
UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison This paper evaluates encoder fine-tuning and synthetic data augmentation for fallacy detection and argument classification on Reddit discussions. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on detecting informal fallacies in Reddit posts, not code.
UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison This paper evaluates encoder fine-tuning and synthetic data augmentation for fallacy detection and argument classification on Reddit discussions. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not cover retrieval systems or knowledge-augmented pipelines.
UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison This paper evaluates encoder fine-tuning and synthetic data augmentation for fallacy detection and argument classification on Reddit discussions. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Reports competition participation results rather than a general evaluation benchmark.
UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison This paper evaluates encoder fine-tuning and synthetic data augmentation for fallacy detection and argument classification on Reddit discussions. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Benchmarks standard encoders and LLMs without proposing compression or efficiency techniques.
UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison This paper evaluates encoder fine-tuning and synthetic data augmentation for fallacy detection and argument classification on Reddit discussions. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses argument classification rather than AI safety or system guardrails.
Towards Universal Calibration for In-Context Classification This paper presents a universal calibrator to improve confidence estimates of in-context classification without requiring model-specific fine-tuning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, distillation, or serving speed.
Towards Universal Calibration for In-Context Classification This paper presents a universal calibrator to improve confidence estimates of in-context classification without requiring model-specific fine-tuning. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text classification rather than multimodal models.
Towards Universal Calibration for In-Context Classification This paper presents a universal calibrator to improve confidence estimates of in-context classification without requiring model-specific fine-tuning. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with general in-context classification, not coding or software tasks.
Towards Universal Calibration for In-Context Classification This paper presents a universal calibrator to improve confidence estimates of in-context classification without requiring model-specific fine-tuning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve external knowledge retrieval or grounding pipelines.
Towards Universal Calibration for In-Context Classification This paper presents a universal calibrator to improve confidence estimates of in-context classification without requiring model-specific fine-tuning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines in-context classification confidence, not agent planning or tool execution.
Gluing Local Contexts into Global Meaning: A Sheaf-Theoretic Decomposition of Transformer Representations This paper proposes a mathematical framework using cellular sheaves to isolate phrasing-invariant content representations in transformer language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates transformer interpretability theory, not LLM agents or tool use.
Gluing Local Contexts into Global Meaning: A Sheaf-Theoretic Decomposition of Transformer Representations This paper proposes a mathematical framework using cellular sheaves to isolate phrasing-invariant content representations in transformer language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies internal representations of language models, not software development.
Gluing Local Contexts into Global Meaning: A Sheaf-Theoretic Decomposition of Transformer Representations This paper proposes a mathematical framework using cellular sheaves to isolate phrasing-invariant content representations in transformer language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Examines internal representation geometry, not model compression or efficiency.
Gluing Local Contexts into Global Meaning: A Sheaf-Theoretic Decomposition of Transformer Representations This paper proposes a mathematical framework using cellular sheaves to isolate phrasing-invariant content representations in transformer language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Examines text transformer representations without multimodal components.
Gluing Local Contexts into Global Meaning: A Sheaf-Theoretic Decomposition of Transformer Representations This paper proposes a mathematical framework using cellular sheaves to isolate phrasing-invariant content representations in transformer language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical interpretability study not directly focused on safety guardrails or robustness.
Gluing Local Contexts into Global Meaning: A Sheaf-Theoretic Decomposition of Transformer Representations This paper proposes a mathematical framework using cellular sheaves to isolate phrasing-invariant content representations in transformer language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not deal with retrieval-augmented generation architectures.
Gluing Local Contexts into Global Meaning: A Sheaf-Theoretic Decomposition of Transformer Representations This paper proposes a mathematical framework using cellular sheaves to isolate phrasing-invariant content representations in transformer language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an interpretability framework rather than benchmark evaluation.
ToolHACE: A Dataset for Detecting Hallucinations in Tool-Based LLM Responses This paper introduces a benchmark dataset to evaluate hallucination detection in language model responses after external tool execution. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on general tool-augmented dialogue, not code generation or repair.
ToolHACE: A Dataset for Detecting Hallucinations in Tool-Based LLM Responses This paper introduces a benchmark dataset to evaluate hallucination detection in language model responses after external tool execution. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with text and tool responses, not multimodal vision/audio tasks.
ToolHACE: A Dataset for Detecting Hallucinations in Tool-Based LLM Responses This paper introduces a benchmark dataset to evaluate hallucination detection in language model responses after external tool execution. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on tool-calling outputs in dialogue rather than document retrieval or RAG.
ToolHACE: A Dataset for Detecting Hallucinations in Tool-Based LLM Responses This paper introduces a benchmark dataset to evaluate hallucination detection in language model responses after external tool execution. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on small models, quantization, distillation, or serving efficiency.
DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck The paper proposes an agent framework that discovers lightweight proxy evaluators to bypass costly benchmark evaluations during autonomous engineering discovery. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses search and evaluation scaling rather than model compression or efficient serving.
DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck The paper proposes an agent framework that discovers lightweight proxy evaluators to bypass costly benchmark evaluations during autonomous engineering discovery. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or external document indexing.
DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck The paper proposes an agent framework that discovers lightweight proxy evaluators to bypass costly benchmark evaluations during autonomous engineering discovery. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on software engineering code agents, not multimodal understanding.
DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck The paper proposes an agent framework that discovers lightweight proxy evaluators to bypass costly benchmark evaluations during autonomous engineering discovery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses optimization efficiency rather than safety risks, jailbreaks, or deployment guardrails.
Soft Teacher Forcing The paper proposes combining teacher-forced embeddings with soft tokens to improve supervised fine-tuning on reasoning datasets without changing inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training method, not a benchmark or evaluation methodology.
Soft Teacher Forcing The paper proposes combining teacher-forced embeddings with soft tokens to improve supervised fine-tuning on reasoning datasets without changing inference. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals entirely with text-based language models, not multimodal inputs.
Soft Teacher Forcing The paper proposes combining teacher-forced embeddings with soft tokens to improve supervised fine-tuning on reasoning datasets without changing inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies supervised fine-tuning training efficiency, not model compression or serving.
Soft Teacher Forcing The paper proposes combining teacher-forced embeddings with soft tokens to improve supervised fine-tuning on reasoning datasets without changing inference. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval, external knowledge sources, or grounding.
Soft Teacher Forcing The paper proposes combining teacher-forced embeddings with soft tokens to improve supervised fine-tuning on reasoning datasets without changing inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses training techniques for reasoning, not safety, guardrails, or alignment.
Soft Teacher Forcing The paper proposes combining teacher-forced embeddings with soft tokens to improve supervised fine-tuning on reasoning datasets without changing inference. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluated on math datasets, not code generation or software engineering.
Soft Teacher Forcing The paper proposes combining teacher-forced embeddings with soft tokens to improve supervised fine-tuning on reasoning datasets without changing inference. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on fine-tuning training loss, not agent workflows or multi-step execution.
Welcome to the Age of Agent Ads: Using On-Policy Attacks to Steer Agentic Decisions The paper investigates on-policy indirect manipulation attacks that steer autonomous agent purchasing and ranking decisions via web retrieval. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation.
Welcome to the Age of Agent Ads: Using On-Policy Attacks to Steer Agentic Decisions The paper investigates on-policy indirect manipulation attacks that steer autonomous agent purchasing and ranking decisions via web retrieval. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or inference serving efficiency.
Welcome to the Age of Agent Ads: Using On-Policy Attacks to Steer Agentic Decisions The paper investigates on-policy indirect manipulation attacks that steer autonomous agent purchasing and ranking decisions via web retrieval. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with text-based agent web browsing and ranking, not multimodal models.
Welcome to the Age of Agent Ads: Using On-Policy Attacks to Steer Agentic Decisions The paper investigates on-policy indirect manipulation attacks that steer autonomous agent purchasing and ranking decisions via web retrieval. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on adversarial steering attacks rather than LLM benchmarking methods.
MLAfication: Efficiently Enabling High-Fidelity MLA for Modern LLMs The paper introduces an attention conversion method enabling multi-head latent attention on modern architectures with reduced memory footprints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an architecture optimization method rather than an evaluation benchmark.
MLAfication: Efficiently Enabling High-Fidelity MLA for Modern LLMs The paper introduces an attention conversion method enabling multi-head latent attention on modern architectures with reduced memory footprints. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation architectures.
MLAfication: Efficiently Enabling High-Fidelity MLA for Modern LLMs The paper introduces an attention conversion method enabling multi-head latent attention on modern architectures with reduced memory footprints. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses general LLM attention architecture rather than code generation.
MLAfication: Efficiently Enabling High-Fidelity MLA for Modern LLMs The paper introduces an attention conversion method enabling multi-head latent attention on modern architectures with reduced memory footprints. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses memory and throughput efficiency, not AI safety or alignment.
MLAfication: Efficiently Enabling High-Fidelity MLA for Modern LLMs The paper introduces an attention conversion method enabling multi-head latent attention on modern architectures with reduced memory footprints. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on attention compression rather than multi-step agent execution.
MLAfication: Efficiently Enabling High-Fidelity MLA for Modern LLMs The paper introduces an attention conversion method enabling multi-head latent attention on modern architectures with reduced memory footprints. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on core transformer attention efficiency, not multimodal integration.
Online LLM Personalization from Pairwise Preferences Across Users The paper formulates online language model persona personalization as a dueling bandit problem using sequential pairwise user preferences. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on style personalization rather than knowledge retrieval or RAG.
Online LLM Personalization from Pairwise Preferences Across Users The paper formulates online language model persona personalization as a dueling bandit problem using sequential pairwise user preferences. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, pruning, or serving efficiency.
Online LLM Personalization from Pairwise Preferences Across Users The paper formulates online language model persona personalization as a dueling bandit problem using sequential pairwise user preferences. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to code generation or software engineering.
Online LLM Personalization from Pairwise Preferences Across Users The paper formulates online language model persona personalization as a dueling bandit problem using sequential pairwise user preferences. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on style personalization rather than safety, guardrails, or alignment risks.
Online LLM Personalization from Pairwise Preferences Across Users The paper formulates online language model persona personalization as a dueling bandit problem using sequential pairwise user preferences. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with text tutoring prompts, not multimodal applications.
Online LLM Personalization from Pairwise Preferences Across Users The paper formulates online language model persona personalization as a dueling bandit problem using sequential pairwise user preferences. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates prompt persona selection via dueling bandits, not agentic planning or tools.
Nufo: A Benchmark for Ewe Automatic Speech Recognition The paper introduces a 174-hour speech benchmark and evaluates baseline models on Ewe automatic speech recognition. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Unrelated to software engineering or programming tasks.
Nufo: A Benchmark for Ewe Automatic Speech Recognition The paper introduces a 174-hour speech benchmark and evaluates baseline models on Ewe automatic speech recognition. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation.
Nufo: A Benchmark for Ewe Automatic Speech Recognition The paper introduces a 174-hour speech benchmark and evaluates baseline models on Ewe automatic speech recognition. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Speech recognition dataset, outside the scope of multimodal vision-language models.

11,161–11,220 of 17,313← Previous Next →