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
Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders The paper presents a system using technical language processing to transform industrial maintenance work order records into an ontology. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on knowledge extraction and verification rather than AI safety or guardrails.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation or external document grounding.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies existing speech leaderboards rather than proposing LLM benchmarks or evaluation methods.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses ASR distillation trade-offs rather than safety, alignment, or guardrail systems.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses purely on automatic speech recognition distillation, not multimodal cross-modal reasoning.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on speech recognition model distillation rather than agent planning or tool use.
CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition The paper proposes confining feature distillation loss to language-routed adapters to balance performance in compact multilingual speech recognition models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Concerns speech recognition, not software engineering or code generation.
Graph-Based Orchestration of Heterogeneous AI Models: A Control Node Approach to Composite Intelligence The paper presents a graph-based framework using control nodes and behavior trees to coordinate heterogeneous artificial intelligence models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on system composition and execution graphs rather than safety or reliability guardrails.
Graph-Based Orchestration of Heterogeneous AI Models: A Control Node Approach to Composite Intelligence The paper presents a graph-based framework using control nodes and behavior trees to coordinate heterogeneous artificial intelligence models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a system orchestration architecture rather than an evaluation benchmark or methodology.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Tests operational synthesis from provided document packets rather than external retrieval systems.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates long-form planning determinations without multi-step autonomous tool use or execution.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Exclusively uses textual operational document scenarios without multimodal inputs.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Tests planning capability rather than deployment safety, guardrails, or alignment risks.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on operational planning from text packets, not code generation or software repair.
MA-Bench: Measuring Operational Planning in Language Models The paper introduces a benchmark assessing language models on operational planning from incomplete, complex, and contradictory military document sets. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Benchmark study that does not address model compression, pruning, or efficient inference.
Compiling Expert Procedures into Verified Programs: When a Small Model Is Enough The paper compiles teacher model reasoning traces into verified programs that guide small models through complex procedural tool-use tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Aims to boost tool-use capability rather than studying safety risks, jailbreaks, or guardrails.
Compiling Expert Procedures into Verified Programs: When a Small Model Is Enough The paper compiles teacher model reasoning traces into verified programs that guide small models through complex procedural tool-use tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on a procedural execution system rather than benchmarking methodologies.
Into the Unknown: Benchmarking AI agents on open scientific research problems The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Assesses frontier research capabilities, not model compression or efficient inference methods.
Into the Unknown: Benchmarking AI agents on open scientific research problems The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on end-to-end agentic research rather than specific retrieval-augmented generation architectures.
Into the Unknown: Benchmarking AI agents on open scientific research problems The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmark paper examining research capabilities rather than deployment safety or jailbreak prevention.
Into the Unknown: Benchmarking AI agents on open scientific research problems The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates scientific research paper generation rather than software engineering or code assistants.
Into the Unknown: Benchmarking AI agents on open scientific research problems The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on multimodal reasoning across vision, audio, or document models.
ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models The paper introduces an evergreen benchmark to evaluate the ability of language models to retrieve and format multi-element factual lists. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on static question-answering evaluation rather than multi-step tool-use agents.
ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models The paper introduces an evergreen benchmark to evaluate the ability of language models to retrieve and format multi-element factual lists. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Evaluates parametric memory retrieval in LLMs, not retrieval-augmented generation systems.
ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models The paper introduces an evergreen benchmark to evaluate the ability of language models to retrieve and format multi-element factual lists. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Restricted purely to text-based factual question answering without multimodal components.
ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models The paper introduces an evergreen benchmark to evaluate the ability of language models to retrieve and format multi-element factual lists. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on benchmark creation rather than model compression, quantization, or efficient serving.
ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models The paper introduces an evergreen benchmark to evaluate the ability of language models to retrieve and format multi-element factual lists. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not address software engineering, code generation, or programming assistants.
ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models The paper introduces an evergreen benchmark to evaluate the ability of language models to retrieve and format multi-element factual lists. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Measures factual recall accuracy rather than safety risks, red-teaming, or deployment guardrails.
Verification Turns Capability Gaps into Routing Decisions: Small Language Models for Clinical Documentation The paper studies using an agentic verification architecture to enable small on-device models to generate factual clinical documentation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies a verifier architecture to clinical note generation rather than benchmarking LLMs.
Learning from Self-Generated Responses: Reliability--Uncertainty Self-Distillation for Reasoning The paper proposes a self-distillation method weighting loss by judged reliability and student predictive uncertainty on open-ended reasoning tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates a training method across existing benchmarks rather than benchmarking LLM systems.
Learning from Self-Generated Responses: Reliability--Uncertainty Self-Distillation for Reasoning The paper proposes a self-distillation method weighting loss by judged reliability and student predictive uncertainty on open-ended reasoning tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on self-distillation training rather than multi-step tool-using autonomous agents.
Learning from Self-Generated Responses: Reliability--Uncertainty Self-Distillation for Reasoning The paper proposes a self-distillation method weighting loss by judged reliability and student predictive uncertainty on open-ended reasoning tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses reasoning fine-tuning via self-distillation rather than deployment safety or guardrails.
Multi-System Cognitive Steering for Generative Communication The paper presents a framework to steer language models using multimodal context to expand abbreviated text inputs while preserving user intent. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on assistive generative communication rather than AI safety risks or guardrails.
Multi-System Cognitive Steering for Generative Communication The paper presents a framework to steer language models using multimodal context to expand abbreviated text inputs while preserving user intent. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates simulated abbreviation expansion rather than introducing a general LLM benchmark.
Multi-System Cognitive Steering for Generative Communication The paper presents a framework to steer language models using multimodal context to expand abbreviated text inputs while preserving user intent. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses intent expansion from sparse inputs rather than tool use or autonomous agents.
Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection The paper introduces a diagnostic pipeline to identify low-confidence patient subgroups and detect harmful covariate shifts in clinical prediction models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical tabular/prediction model shifts rather than LLM-based autonomous agents.
Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection The paper introduces a diagnostic pipeline to identify low-confidence patient subgroups and detect harmful covariate shifts in clinical prediction models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, distillation, quantization, or efficient deployment.
Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection The paper introduces a diagnostic pipeline to identify low-confidence patient subgroups and detect harmful covariate shifts in clinical prediction models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Deals with subgroup covariate shift detection in clinical predictive models, not LLMs.
Identity, Incentives, and Cooperative Behavior for AI: Reverse-Engineering Multiagent Cooperation The paper develops an analytical framework to interpret and reverse-engineer incentives and cooperative behaviors in multi-agent artificial intelligence systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Provides experimental designs for multi-agent cooperation rather than standardized LLM evaluation benchmarks.
Embedding Geometry Shapes LLM Routing: Tracing Failure Modes and Improving Generalization The paper uses intermediate transformer hidden states instead of semantic embeddings to improve classifier-based prompt routing across unseen tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on routing classifier algorithms rather than LLM evaluation benchmarks or methodologies.
Embedding Geometry Shapes LLM Routing: Tracing Failure Modes and Improving Generalization The paper uses intermediate transformer hidden states instead of semantic embeddings to improve classifier-based prompt routing across unseen tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies prompt-based model routing classifiers rather than multi-step autonomous agent systems.
Embedding Geometry Shapes LLM Routing: Tracing Failure Modes and Improving Generalization The paper uses intermediate transformer hidden states instead of semantic embeddings to improve classifier-based prompt routing across unseen tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Analyzes model routing accuracy across costs rather than safety, red-teaming, or guardrails.
Finite-sample certification of coverage regret for budgeted document selection under adaptive stratified auditing The paper derives statistical confidence bounds on coverage regret for selecting budget-constrained document subsets in retrieval systems under adaptive auditing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with mathematical coverage guarantees rather than operational safety or guardrails.
Finite-sample certification of coverage regret for budgeted document selection under adaptive stratified auditing The paper derives statistical confidence bounds on coverage regret for selecting budget-constrained document subsets in retrieval systems under adaptive auditing. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents statistical bounds for document selection, not multi-step autonomous planning agents.
Finite-sample certification of coverage regret for budgeted document selection under adaptive stratified auditing The paper derives statistical confidence bounds on coverage regret for selecting budget-constrained document subsets in retrieval systems under adaptive auditing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on statistical audit bounds rather than LLM evaluation or benchmark design.
Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules The paper presents a method to retrofit attention models with hierarchical test-time neural memory modules to improve long-context inference efficiency. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies internal neural memory modules for long context, not external document retrieval.
Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules The paper presents a method to retrofit attention models with hierarchical test-time neural memory modules to improve long-context inference efficiency. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses solely on text-based language model attention and context architectures.
Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules The paper presents a method to retrofit attention models with hierarchical test-time neural memory modules to improve long-context inference efficiency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates on standard context benchmarks without proposing new evaluation methodologies or benchmarks.
Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules The paper presents a method to retrofit attention models with hierarchical test-time neural memory modules to improve long-context inference efficiency. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not focus on code generation, software assistants, or program repair.
Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules The paper presents a method to retrofit attention models with hierarchical test-time neural memory modules to improve long-context inference efficiency. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes an architectural memory retrofit for LLMs rather than autonomous agent systems.
Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules The paper presents a method to retrofit attention models with hierarchical test-time neural memory modules to improve long-context inference efficiency. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses context efficiency and memory architectures, not deployment safety or robustness.
Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings The paper provides theoretical conditions and algorithms for preserving causal graph identifiability when working with missing data distributions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Concerns statistical causal discovery theory rather than LLM safety, guardrails, or hallucination.
Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings The paper provides theoretical conditions and algorithms for preserving causal graph identifiability when working with missing data distributions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on causal estimation theory, not model efficiency, distillation, or quantization.
Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings The paper provides theoretical conditions and algorithms for preserving causal graph identifiability when working with missing data distributions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Deals with causal graph identifiability, not LLM evaluation benchmarks or methodologies.
Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings The paper provides theoretical conditions and algorithms for preserving causal graph identifiability when working with missing data distributions. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Theoretical statistics paper on causal discovery, completely unrelated to AI agents.
PREFER: From Player Experience to Personalized Game Refinement The paper introduces a framework using psychometric dimensions and player personas to benchmark how language models evaluate and refine video games. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses game design refinement rather than AI safety, reliability, or deployment risks.
PREFER: From Player Experience to Personalized Game Refinement The paper introduces a framework using psychometric dimensions and player personas to benchmark how language models evaluate and refine video games. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on simulated personas for game testing rather than multi-step tool-using agents.
Narrowing the Collaboration Gap, Probably The paper examines communicating calibrated action probabilities rather than direct actions to improve multi-agent collaboration under partial information. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates agent coordination efficiency rather than deployment safety, red-teaming, or guardrails.
Narrowing the Collaboration Gap, Probably The paper examines communicating calibrated action probabilities rather than direct actions to improve multi-agent collaboration under partial information. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies multi-agent communication mechanisms rather than benchmark design or general evaluation methodologies.
Narrowing the Collaboration Gap, Probably The paper examines communicating calibrated action probabilities rather than direct actions to improve multi-agent collaboration under partial information. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, distillation, or efficient model serving.

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