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
Label Collapse in Diffusion Language Model In-Context Learning This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation systems.
Label Collapse in Diffusion Language Model In-Context Learning This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies diffusion and autoregressive model biases, not compression or serving efficiency.
ANCHOR: Assessing Naturalistic Constraint Hierarchies and Ordering in Repository-Grounded Agentic Coding This paper introduces a repository-grounded benchmark to evaluate how well coding agents follow engineering constraints and developer workflows. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Evaluates agent compliance on software tasks, not model compression or efficiency.
ANCHOR: Assessing Naturalistic Constraint Hierarchies and Ordering in Repository-Grounded Agentic Coding This paper introduces a repository-grounded benchmark to evaluate how well coding agents follow engineering constraints and developer workflows. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not focus on retrieval-augmented generation architectures.
ANCHOR: Assessing Naturalistic Constraint Hierarchies and Ordering in Repository-Grounded Agentic Coding This paper introduces a repository-grounded benchmark to evaluate how well coding agents follow engineering constraints and developer workflows. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates code and repository tasks without multimodal perception.
SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications? This paper introduces a benchmark to test if coding agents can build reliable validation test harnesses from natural language specifications. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on test harness generation rather than external document retrieval.
SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications? This paper introduces a benchmark to test if coding agents can build reliable validation test harnesses from natural language specifications. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Pertains to software repositories and specifications, not multimodal data.
SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications? This paper introduces a benchmark to test if coding agents can build reliable validation test harnesses from natural language specifications. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on evaluating agent testing capabilities, not model compression or efficiency.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not examine model compression, pruning, or efficient serving frameworks.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation pipelines or document retrieval.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on uncertainty estimation techniques rather than benchmarking LLMs.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on uncertainty quantification of model responses rather than agentic planning.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses multimodal uncertainty quantification, not software code generation.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval systems, databases, or grounding.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies prompt-level word meaning integration, not autonomous agentic systems.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies lexical semantics in prompts, not code generation or software engineering.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses strictly on text instruction following and semantics.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate compression, quantization, distillation, or serving speed.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, quantization, or serving techniques.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on cognitive event modeling, not retrieval-augmented generation.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a cognitive modeling framework, not an LLM benchmark or evaluation protocol.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Analyzes text interview narratives without multimodal data.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Applies models to cognitive memory research, not AI safety or deployment robustness.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses LLMs for narrative extraction, not autonomous planning or tool execution.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on narrative and episodic memory analysis, not code generation.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies user behavior prediction on Reddit rather than AI system safety and reliability.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with mental health prediction, not code generation or program repair.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on survival analysis of Reddit user data, not agentic LLMs.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals only with text and behavioral features on social media.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, quantization, or inference optimization.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a domain-specific predictive modeling study rather than an LLM benchmark.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address document retrieval or retrieval-augmented generation.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a runtime shielding framework rather than a general evaluation benchmark.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or inference speed.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Applies to text-based tool-use agents, not multimodal systems.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on procedural compliance and shielding rather than retrieval-augmented generation.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Targets general workflow policies (e.g., travel booking), not software engineering or coding.
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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates poetry and academic text predictability, not software engineering.
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.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies cross-entropy rankings without focusing on safety, hallucination, or guardrails.
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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies text surprisal and predictability, not multimodal architectures.
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.
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	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.
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.
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	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	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	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	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	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	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.

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