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
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	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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Assesses frontier research capabilities, not model compression or efficient inference methods.
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	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	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	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	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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Deals with subgroup covariate shift detection in clinical predictive models, not LLMs.
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.
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	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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on routing classifier algorithms rather than LLM evaluation benchmarks or methodologies.
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.
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.
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	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	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.
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	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	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not focus on code generation, software assistants, or program repair.
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.
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	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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on causal estimation theory, not model efficiency, distillation, or quantization.
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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, distillation, or efficient model serving.
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.
REPORT-OPS: Hybrid Evidence-Guided Reporting with Human-Supervised Refinement for HPC The paper presents an agent that combines language models with operational policies to generate verified operational reports for high-performance computing systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an HPC reporting agent rather than general LLM evaluation benchmarks.
Definition Grounding in Theory-Level Auto-formalization The paper proposes definition grounding to continuously evaluate and revise formal mathematical definitions generated by language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on formal mathematical verification rather than operational AI deployment safety.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on mechanistic interpretability of syntax heads rather than benchmark evaluation.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies attention head interpretability rather than model compression or efficient serving.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies internal syntax mechanisms rather than retrieval-augmented generation systems.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on linguistic interpretability rather than deployment safety, jailbreaks, or guardrails.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Studies textual syntax heads rather than multimodal vision-language understanding.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes linguistic syntax heads rather than code generation or program repair.
Do Syntax Heads Influence Model Behavior? The paper analyzes whether syntactic attention heads in transformers play a direct causal role in syntactic language predictions. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates internal transformer attention heads rather than autonomous planning agents.
Write Once, Plan Many: Building Reusable Visual Planning Systems from One Scene The paper presents a framework using vision-language models to generate reusable visual planning code from a single practice scene. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on visual planning synthesis rather than LLM evaluation benchmarks.
Write Once, Plan Many: Building Reusable Visual Planning Systems from One Scene The paper presents a framework using vision-language models to generate reusable visual planning code from a single practice scene. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on planning program generation rather than safety guardrails or alignment.
Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction The paper develops an efficient two-pass scheduling method to update nearest-neighbor graphs in SE(3)-equivariant structure prediction models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on geometric graph algorithm compensation rather than AI safety guardrails.
Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction The paper develops an efficient two-pass scheduling method to update nearest-neighbor graphs in SE(3)-equivariant structure prediction models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Optimizes SE(3) graph prediction pipelines rather than efficient language model inference.
Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction The paper develops an efficient two-pass scheduling method to update nearest-neighbor graphs in SE(3)-equivariant structure prediction models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Deals with equivariant structure prediction pipelines, not AI planning agents.
Refined Topology-Pass Scheduling: Damped Trust-Region Compensation for Dynamic k-NN Graphs in SE(3)-Equivariant Structure Prediction The paper develops an efficient two-pass scheduling method to update nearest-neighbor graphs in SE(3)-equivariant structure prediction models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on molecular structure graph optimization rather than LLM benchmarks.
Copy-Paste Language Models The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Proposes architectural copying heads rather than AI safety or reliability guardrails.
Copy-Paste Language Models The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Proposes a general language modeling head rather than software engineering code models.
Copy-Paste Language Models The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on textual token copying heads rather than multimodal understanding.
Copy-Paste Language Models The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Proposes a model architecture head rather than multi-step planning or autonomous agents.

3,841–3,900 of 13,754← Previous Next →