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
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on model architecture for in-context copying rather than evaluation benchmarks.
MIND: Instruction-Conditioned Incremental Grounding for Robots The paper introduces a framework that incrementally updates robot spatial judgments after environmental changes without full scene recomputation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic target maintenance efficiency rather than deployment safety guardrails.
MIND: Instruction-Conditioned Incremental Grounding for Robots The paper introduces a framework that incrementally updates robot spatial judgments after environmental changes without full scene recomputation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic navigation and room grounding, not industrial defect detection.
MIND: Instruction-Conditioned Incremental Grounding for Robots The paper introduces a framework that incrementally updates robot spatial judgments after environmental changes without full scene recomputation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotic instruction grounding rather than LLM benchmark methodologies.
Beyond Maliciousness: Static and Runtime Attack-Surface Profiling of Agent Skills The paper profiles the security attack surface and execution privileges of third-party agent skills across public marketplaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on security profiling of agent tools rather than general LLM evaluation benchmarks.
MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on meme moderation rather than software development or code generation.
MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on multimodal meme provenance analysis rather than RAG architecture.
MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an agent application rather than benchmark design or general LLM evaluation.
MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on meme analysis using VLMs rather than model compression or efficiency.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Audits general evaluation benchmarks rather than code generation or software engineering tools.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Examines benchmark assumptions rather than retrieval-augmented generation systems.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on benchmark audit methodologies rather than multimodal architecture or reasoning.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Audits evaluation datasets rather than multi-step agent planning or tool use.
Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources The paper introduces a framework to audit cultural, geographic, and user-representation biases embedded within popular machine learning benchmarks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on evaluation benchmark auditing rather than model compression or efficient serving.
Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series The paper evaluates recurrent neural networks and feature attribution methods for tree-species classification using satellite time-series imagery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Applies remote sensing classification rather than general LLM evaluation or benchmarking.
Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series The paper evaluates recurrent neural networks and feature attribution methods for tree-species classification using satellite time-series imagery. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies tree-species classification using RNNs and Sentinel-2 imagery, not AI agents.
Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series The paper evaluates recurrent neural networks and feature attribution methods for tree-species classification using satellite time-series imagery. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Discusses remote sensing RNN classification rather than small language models or model compression.
Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series The paper evaluates recurrent neural networks and feature attribution methods for tree-species classification using satellite time-series imagery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Explores remote sensing interpretability rather than AI safety guardrails or deployment reliability.
Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation The paper establishes theoretical bounds and sample complexity laws for models generalizing across varying input sizes in structured data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of generalization bounds rather than operational safety or robustness in deployment.
Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation The paper establishes theoretical bounds and sample complexity laws for models generalizing across varying input sizes in structured data. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on theoretical statistical learning theory on sets and graphs, not AI agents.
Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation The paper establishes theoretical bounds and sample complexity laws for models generalizing across varying input sizes in structured data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of sample complexity rather than empirical LLM evaluation methods.
Dimension-Free Scaling Laws for Size Generalization: An Exponential Separation The paper establishes theoretical bounds and sample complexity laws for models generalizing across varying input sizes in structured data. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Discusses theoretical sample complexity for size generalization rather than practical efficient model inference.
Whose turn is it? Context-guided model routing for stateful agents The paper investigates context-aware model routing rules across turns in stateful multi-step agent applications. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on model routing optimization rather than benchmark design or LLM evaluation.
Whose turn is it? Context-guided model routing for stateful agents The paper investigates context-aware model routing rules across turns in stateful multi-step agent applications. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on cost reduction and routing efficiency rather than AI safety or guardrails.
Beyond ICD Codes: Semantic IDs for Generative Medical Coding The paper proposes training language models on hierarchically clustered semantic identifiers instead of standard ICD codes for clinical coding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a representation framework for medical coding rather than general benchmark methodologies.
Beyond ICD Codes: Semantic IDs for Generative Medical Coding The paper proposes training language models on hierarchically clustered semantic identifiers instead of standard ICD codes for clinical coding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on clinical document coding accuracy rather than AI safety or jailbreak prevention.
Beyond ICD Codes: Semantic IDs for Generative Medical Coding The paper proposes training language models on hierarchically clustered semantic identifiers instead of standard ICD codes for clinical coding. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses generative medical coding rather than multi-step planning or autonomous agents.
DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervision The paper introduces a distillation framework that triggers teacher takeovers when student policies diverge during long-horizon reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with training stability in distillation rather than operational safety or guardrails.
DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervision The paper introduces a distillation framework that triggers teacher takeovers when student policies diverge during long-horizon reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a model distillation training method rather than an evaluation benchmark.
Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems The paper provides a theoretical generalization and stability analysis of biased stochastic gradient methods for minimax optimization problems. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Theoretical optimization paper on stochastic gradient methods, not AI agents.
Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems The paper provides a theoretical generalization and stability analysis of biased stochastic gradient methods for minimax optimization problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Presents abstract optimization theory rather than practical deployment safety or guardrails.
Generalization Analysis of Biased Stochastic Gradient Methods for Minimax Problems The paper provides a theoretical generalization and stability analysis of biased stochastic gradient methods for minimax optimization problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Mathematical analysis of minimax optimization rather than empirical LLM benchmark evaluation.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on model architecture and pretraining transparency rather than safety guardrails or alignment.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Contains standard model benchmarks rather than proposing new evaluation methodologies.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Introduces a general sovereign language model rather than tools for software engineering.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Covers foundational model training, not retrieval-augmented generation techniques.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text language models without multimodal cross-domain capabilities.
LLM-jp-4 Technical Report The paper details the development, training data criteria, and performance of the open Japanese sovereign models, LLM-jp-4. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Describes base LLM pretraining rather than autonomous multi-step agents or tool use.
Can We Believe AI Peer Review? The paper evaluates the reliability, score variance, and consistency of automated peer reviews generated by large language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies LLMs used for scientific reviewing rather than multi-step tool-using agents.
Why Do Agent Teams Fail Across Boundaries? A Failure Taxonomy for Agent-to-Agent Systems with Private States The paper presents a taxonomy of failure modes in multi-agent systems where agents interact across organizational boundaries with private states. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on multi-agent failure taxonomy rather than general LLM evaluation benchmarks.
When Is Smaller Greener? Right-Sizing Language Models for Agentic AI The paper presents a decision model to evaluate language model efficiency based on energy and cost per completed agentic task. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a sizing decision model rather than an evaluation benchmark.
When Is Smaller Greener? Right-Sizing Language Models for Agentic AI The paper presents a decision model to evaluate language model efficiency based on energy and cost per completed agentic task. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on energy and cost optimization rather than deployment safety risks.
Asymptotic Behavior of Transformers The paper mathematically analyzes long-sequence generation in transformers, establishing temperature thresholds required for internal cache convergence. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Mathematical proof of convergence properties, not an empirical benchmark.
Asymptotic Behavior of Transformers The paper mathematically analyzes long-sequence generation in transformers, establishing temperature thresholds required for internal cache convergence. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on asymptotic convergence properties, not model compression or serving.
Asymptotic Behavior of Transformers The paper mathematically analyzes long-sequence generation in transformers, establishing temperature thresholds required for internal cache convergence. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Mathematical analysis of attention dynamics, not autonomous agent systems.
Steering Vectors for Competing Ethical Frameworks Are Interchangeable: A Competing-Target Test for Activation Steering The paper introduces a test showing that steering vectors designed for competing ethical frameworks behave interchangeably in open-weight models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes ethical steering vectors, not code generation or program repair.
Steering Vectors for Competing Ethical Frameworks Are Interchangeable: A Competing-Target Test for Activation Steering The paper introduces a test showing that steering vectors designed for competing ethical frameworks behave interchangeably in open-weight models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Deals with internal representation steering, not external document retrieval.
Steering Vectors for Competing Ethical Frameworks Are Interchangeable: A Competing-Target Test for Activation Steering The paper introduces a test showing that steering vectors designed for competing ethical frameworks behave interchangeably in open-weight models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies activation steering vectors, not autonomous multi-step agents.
Steering Vectors for Competing Ethical Frameworks Are Interchangeable: A Competing-Target Test for Activation Steering The paper introduces a test showing that steering vectors designed for competing ethical frameworks behave interchangeably in open-weight models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Examines text model representation steering, not multimodal perception.
Steering Vectors for Competing Ethical Frameworks Are Interchangeable: A Competing-Target Test for Activation Steering The paper introduces a test showing that steering vectors designed for competing ethical frameworks behave interchangeably in open-weight models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on activation steering evaluation, not model efficiency or quantization.
Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems The paper analyzes Best-of-N sampling as an extreme-value competition between tails of correct and incorrect responses to explain reward hacking. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of sampling regimes, not multi-step tool-using agents.
Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems The paper analyzes Best-of-N sampling as an extreme-value competition between tails of correct and incorrect responses to explain reward hacking. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Examines Best-of-N reward sampling, not document retrieval or RAG.
Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems The paper analyzes Best-of-N sampling as an extreme-value competition between tails of correct and incorrect responses to explain reward hacking. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Theoretical analysis of reward modeling, not code assistants or software repair.
Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems The paper analyzes Best-of-N sampling as an extreme-value competition between tails of correct and incorrect responses to explain reward hacking. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with reward scaling theory, not multimodal data.
Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems The paper analyzes Best-of-N sampling as an extreme-value competition between tails of correct and incorrect responses to explain reward hacking. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies sampling theory rather than model compression or efficient serving.
DANGERLENS: Probing Dangerous Representations Hidden by Safety Alignment The paper introduces an internal probing method to detect whether safety-aligned language models still retain accessible representations of hazardous concepts. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Examines internal safety representation audits, not agent planning or tool use.
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Provides a conceptual survey framework rather than an evaluation benchmark.
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Broad survey of multimodal agents, not industrial defect detection.
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on memory representations rather than small or quantized models.
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent memory taxonomy, not deployment safety or attacks.

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