Every paper checked — nothing hidden

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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
Exploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models Studies how depth-based early-exit inference affects language model safety, finding shallow exits resist jailbreaks better than deep exits. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on inference depth and jailbreaks, not retrieval systems.
Exploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models Studies how depth-based early-exit inference affects language model safety, finding shallow exits resist jailbreaks better than deep exits. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text language model inference, not multimodal architectures.
Exploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models Studies how depth-based early-exit inference affects language model safety, finding shallow exits resist jailbreaks better than deep exits. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes vulnerability properties rather than presenting an LLM benchmark.
Exploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models Studies how depth-based early-exit inference affects language model safety, finding shallow exits resist jailbreaks better than deep exits. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses model safety and inference depth, not code generation.
Exploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models Studies how depth-based early-exit inference affects language model safety, finding shallow exits resist jailbreaks better than deep exits. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies dynamic inference and jailbreak attacks, not autonomous agents.
PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces Presents a multi-agent framework that decouples research planning, subquestion investigation, and factual verification via a shared semantic workspace. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on research coordination rather than deployment safety guardrails or jailbreaks.
PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces Presents a multi-agent framework that decouples research planning, subquestion investigation, and factual verification via a shared semantic workspace. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a deep-research multi-agent framework rather than an evaluation benchmark.
PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces Presents a multi-agent framework that decouples research planning, subquestion investigation, and factual verification via a shared semantic workspace. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with research workflows, not small models or quantization techniques.
PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces Presents a multi-agent framework that decouples research planning, subquestion investigation, and factual verification via a shared semantic workspace. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses information search and research agents, not code generation or repair.
PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces Presents a multi-agent framework that decouples research planning, subquestion investigation, and factual verification via a shared semantic workspace. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with text search and reasoning corpora, not multimodal models.
Running Fewer Loops Removes Refusal Behaviour in Recurrent-Depth Language Models Shows that running fewer layer loops in recurrent-depth models significantly reduces safety refusals while preserving most reasoning performance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies depth reduction in language models, not autonomous agent systems.
Running Fewer Loops Removes Refusal Behaviour in Recurrent-Depth Language Models Shows that running fewer layer loops in recurrent-depth models significantly reduces safety refusals while preserving most reasoning performance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates internal refusal mechanisms rather than proposing an LLM benchmark.
HarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems Proposes a hierarchical evolution framework that allows individual agent software harnesses in multi-agent systems to adapt using feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-agent performance optimization rather than deployment safety guardrails.
HarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems Proposes a hierarchical evolution framework that allows individual agent software harnesses in multi-agent systems to adapt using feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a multi-agent system optimization framework, not an evaluation benchmark.
Understanding Unified VLMs: From Attention Probing to Guidance Tuning for Multimodal Generation Probes attention heads in unified vision-language models to study condition processing and introduces an inference-time guidance tuning method. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies classifier-free guidance mechanisms, not deployment safety or alignment.
Understanding Unified VLMs: From Attention Probing to Guidance Tuning for Multimodal Generation Probes attention heads in unified vision-language models to study condition processing and introduces an inference-time guidance tuning method. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates internal attention mechanisms, not multi-step autonomous planning agents.
Understanding Unified VLMs: From Attention Probing to Guidance Tuning for Multimodal Generation Probes attention heads in unified vision-language models to study condition processing and introduces an inference-time guidance tuning method. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an inference guidance technique rather than an evaluation benchmark.
Words to World: Benchmarking LLMs in Interactive Physical Environments Introduces a closed-loop benchmark testing language models controlling physical robotic hardware and reacting to real sensor feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on physical action capabilities rather than safety alignment or attacks.
Using Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring Applies an educational measurement framework to prompt engineering to improve the validity and reproducibility of automated essay scoring systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Covers prompt engineering methodologies for scoring rather than LLM evaluation benchmarks.
Using Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring Applies an educational measurement framework to prompt engineering to improve the validity and reproducibility of automated essay scoring systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses educational prompt design validity, not safety guardrails or red-teaming.
Using Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring Applies an educational measurement framework to prompt engineering to improve the validity and reproducibility of automated essay scoring systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on prompt engineering principles, not multi-step agent workflows.
MoMaTune: Model, Match, Then Tune Language Models for Numerical Records Presents a two-stage post-training strategy that improves language model handling of tabular numerical records using auxiliary pretraining and token matching. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses numerical representation accuracy rather than safety risks or alignment.
MoMaTune: Model, Match, Then Tune Language Models for Numerical Records Presents a two-stage post-training strategy that improves language model handling of tabular numerical records using auxiliary pretraining and token matching. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a fine-tuning methodology rather than evaluation methodologies or benchmarks.
MoMaTune: Model, Match, Then Tune Language Models for Numerical Records Presents a two-stage post-training strategy that improves language model handling of tabular numerical records using auxiliary pretraining and token matching. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on tabular pretraining objectives, not multi-step agent planning.
Framework for an AI-Ready ARPES Community Database Proposes a data-sharing ecosystem and framework to make angle-resolved photoemission spectroscopy data organized and accessible for AI research. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Discusses experimental data sharing rather than LLM benchmarking.
Framework for an AI-Ready ARPES Community Database Proposes a data-sharing ecosystem and framework to make angle-resolved photoemission spectroscopy data organized and accessible for AI research. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a scientific data infrastructure, not an AI agent system.
Framework for an AI-Ready ARPES Community Database Proposes a data-sharing ecosystem and framework to make angle-resolved photoemission spectroscopy data organized and accessible for AI research. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers spectroscopy data curation, not deployment safety or jailbreak defense.
ClinEdge: An Expert-Graded Benchmark of Real Patient Edge Cases Where Frontier Multimodal Models Fail Introduces a medical benchmark of real de-identified patient edge cases pairing imaging with clinical vignettes to test multimodal language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates medical diagnostic models rather than tool-using autonomous agents.
ClinEdge: An Expert-Graded Benchmark of Real Patient Edge Cases Where Frontier Multimodal Models Fail Introduces a medical benchmark of real de-identified patient edge cases pairing imaging with clinical vignettes to test multimodal language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on diagnostic benchmark failure rates rather than safety guardrails.
EvolvAttack: Backdooring self Evolving VLM Agents The paper introduces EvolvAttack, demonstrating a backdoor mechanism that activates only after a vision-language agent evolves its memory over time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies backdoor attack vectors rather than general model evaluation methods.
EvolvAttack: Backdooring self Evolving VLM Agents The paper introduces EvolvAttack, demonstrating a backdoor mechanism that activates only after a vision-language agent evolves its memory over time. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on backdoor vulnerabilities rather than model compression or inference efficiency.
Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning The paper introduces Sequent-Prover, training language model agents to perform multi-turn formal reasoning using an external SMT solver scratchpad. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on SMT solver interactions rather than document retrieval or RAG.
Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning The paper introduces Sequent-Prover, training language model agents to perform multi-turn formal reasoning using an external SMT solver scratchpad. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses formal language and text reasoning, not multimodal inputs.
Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning The paper introduces Sequent-Prover, training language model agents to perform multi-turn formal reasoning using an external SMT solver scratchpad. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on formal mathematical reasoning, not safety guardrails or alignment.
Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning The paper introduces Sequent-Prover, training language model agents to perform multi-turn formal reasoning using an external SMT solver scratchpad. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent fine-tuning and reasoning accuracy, not efficiency techniques.
Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning The paper introduces Sequent-Prover, training language model agents to perform multi-turn formal reasoning using an external SMT solver scratchpad. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an agent framework and training method, not a general benchmark.
RECAP: Calibrating Low-Bit Reasoning Models on the History They Read The paper presents RECAP, a post-training quantization method that calibrates reasoning models using dynamic prefixes to reduce error accumulation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on quantizing reasoning models, not agent planning or tools.
RECAP: Calibrating Low-Bit Reasoning Models on the History They Read The paper presents RECAP, a post-training quantization method that calibrates reasoning models using dynamic prefixes to reduce error accumulation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a post-training quantization method rather than benchmark evaluation.
RECAP: Calibrating Low-Bit Reasoning Models on the History They Read The paper presents RECAP, a post-training quantization method that calibrates reasoning models using dynamic prefixes to reduce error accumulation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses quantization accuracy degradation rather than safety or jailbreak risks.
LeHami: Symplectic Latent Dynamics for World Models The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluated on visual pendulum physics simulations, not industrial quality control.
LeHami: Symplectic Latent Dynamics for World Models The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses representation drift in world models, not deployment safety guardrails.
LeHami: Symplectic Latent Dynamics for World Models The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a world model architecture, not an evaluation framework.
LeHami: Symplectic Latent Dynamics for World Models The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies dynamical systems in world models, not LLM-based agent planning.
LeHami: Symplectic Latent Dynamics for World Models The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on symplectic dynamics preservation, not model compression or quantization.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an architectural search framework, not an LLM benchmark.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Investigates general depth estimation and reconstruction, not industrial defect detection.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses purely on computer vision topologies, not multimodal vision-language models.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with neural architecture search for vision, not agent workflows.
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies geometric vision architectures, not AI safety or reliability.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Benchmarks linguistic and cultural understanding rather than safety guardrails or attacks.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on evaluating static LLM QA, not agentic planning.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Presents an evaluation benchmark rather than methods for model efficiency.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates parametric knowledge rather than retrieval-augmented generation systems.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Assesses sociolinguistic and regional knowledge, not code generation or repair.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text-based question answering, not multimodal vision-language models.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on biology microscopy images, not manufacturing or industrial inspection.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on microscopy image segmentation, not autonomous planning or agents.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies biological crystal detection, not AI safety or deployment reliability.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with standard computer vision on microscopy, not multimodal models.
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Proposes color channel segmentation, not model compression or efficient serving.

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