Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
EVA-Cap: Optimizing Audiovisual Video Captioning via Event-Centric Alignment Proposes an event-centric framework to align audio and video features when generating detailed video descriptions with language models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not deal with quantization, distillation, or edge efficiency.
EVA-Cap: Optimizing Audiovisual Video Captioning via Event-Centric Alignment Proposes an event-centric framework to align audio and video features when generating detailed video descriptions with language models. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Has no connection to programming or software tasks.
EVA-Cap: Optimizing Audiovisual Video Captioning via Event-Centric Alignment Proposes an event-centric framework to align audio and video features when generating detailed video descriptions with language models. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Examines general video captioning, not industrial inspection or defect detection.
Hy-Memory-30B-A3B: Attributed Reinforcement Learning for Long-Term Memory Writing Introduces a compact language model trained with distillation and reinforcement learning to manage long-term memory across multiple external frameworks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not target safety guardrails, jailbreaks, or red-teaming.
Hy-Memory-30B-A3B: Attributed Reinforcement Learning for Long-Term Memory Writing Introduces a compact language model trained with distillation and reinforcement learning to manage long-term memory across multiple external frameworks. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Processes textual memory state, not multimodal inputs.
Hy-Memory-30B-A3B: Attributed Reinforcement Learning for Long-Term Memory Writing Introduces a compact language model trained with distillation and reinforcement learning to manage long-term memory across multiple external frameworks. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Does not focus on software engineering or code generation.
Hy-Memory-30B-A3B: Attributed Reinforcement Learning for Long-Term Memory Writing Introduces a compact language model trained with distillation and reinforcement learning to manage long-term memory across multiple external frameworks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a memory model rather than an evaluation standard.
Hy-Memory-30B-A3B: Attributed Reinforcement Learning for Long-Term Memory Writing Introduces a compact language model trained with distillation and reinforcement learning to manage long-term memory across multiple external frameworks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant A 30B parameter model is not focused on compact efficiency.
Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness Analyzes the overuse and contextual appropriateness of a common corrective phrasing pattern in Mandarin language model text. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Analyzes linguistic writing patterns, not agentic planning or execution.
Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness Analyzes the overuse and contextual appropriateness of a common corrective phrasing pattern in Mandarin language model text. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not involve external retrieval or knowledge retrieval.
Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness Analyzes the overuse and contextual appropriateness of a common corrective phrasing pattern in Mandarin language model text. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses entirely on text-based Mandarin linguistic patterns.
Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness Analyzes the overuse and contextual appropriateness of a common corrective phrasing pattern in Mandarin language model text. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Analyzes stylistic linguistic tics rather than deployment safety risks.
Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness Analyzes the overuse and contextual appropriateness of a common corrective phrasing pattern in Mandarin language model text. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Examines natural language writing patterns, not code generation.
Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness Analyzes the overuse and contextual appropriateness of a common corrective phrasing pattern in Mandarin language model text. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address parameter efficiency or model compression.
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty Presents an adversarial training approach designed to improve language model honesty across diverse benchmarks without degrading core capabilities. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Evaluates honesty behaviors, with multi-agent games only as a testbed.
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty Presents an adversarial training approach designed to improve language model honesty across diverse benchmarks without degrading core capabilities. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not investigate retrieval or knowledge-base grounding.
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty Presents an adversarial training approach designed to improve language model honesty across diverse benchmarks without degrading core capabilities. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not cover efficiency, model size reduction, or quantization.
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty Presents an adversarial training approach designed to improve language model honesty across diverse benchmarks without degrading core capabilities. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses primarily on adversarial training methods rather than benchmarking.
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty Presents an adversarial training approach designed to improve language model honesty across diverse benchmarks without degrading core capabilities. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Has no connection to programming or software engineering.
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty Presents an adversarial training approach designed to improve language model honesty across diverse benchmarks without degrading core capabilities. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Involves text-only language models.
Multi-Target Few-Shot Prompting with Diffusion Language Models Uses diffusion language models to perform multi-target few-shot infilling across shared code context guided by demonstration examples. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Addresses prompting diffusion LMs rather than tool-using agents.
Multi-Target Few-Shot Prompting with Diffusion Language Models Uses diffusion language models to perform multi-target few-shot infilling across shared code context guided by demonstration examples. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not address safety, jailbreaks, or alignment monitoring.
Multi-Target Few-Shot Prompting with Diffusion Language Models Uses diffusion language models to perform multi-target few-shot infilling across shared code context guided by demonstration examples. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a prompting method rather than an evaluation framework.
Multi-Target Few-Shot Prompting with Diffusion Language Models Uses diffusion language models to perform multi-target few-shot infilling across shared code context guided by demonstration examples. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on text and code diffusion, not multimodal learning.
Multi-Target Few-Shot Prompting with Diffusion Language Models Uses diffusion language models to perform multi-target few-shot infilling across shared code context guided by demonstration examples. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not study retrieval-augmented generation systems.
Multi-Target Few-Shot Prompting with Diffusion Language Models Uses diffusion language models to perform multi-target few-shot infilling across shared code context guided by demonstration examples. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not investigate model compression, distillation, or efficiency.
Grid-Bench: Can LLMs Predict How Discrete Worlds Change? Evaluates the ability of language models to predict state changes in discrete spatial grids across sequential operations. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not related to code generation, maintenance, or repair.
Grid-Bench: Can LLMs Predict How Discrete Worlds Change? Evaluates the ability of language models to predict state changes in discrete spatial grids across sequential operations. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Measures state-tracking reasoning rather than interactive multi-step agents.
Grid-Bench: Can LLMs Predict How Discrete Worlds Change? Evaluates the ability of language models to predict state changes in discrete spatial grids across sequential operations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not study small architectures, compression, or quantization.
Grid-Bench: Can LLMs Predict How Discrete Worlds Change? Evaluates the ability of language models to predict state changes in discrete spatial grids across sequential operations. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Evaluates text-based spatial representations without multimodal inputs.
Grid-Bench: Can LLMs Predict How Discrete Worlds Change? Evaluates the ability of language models to predict state changes in discrete spatial grids across sequential operations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not study external document retrieval or grounding.
Grid-Bench: Can LLMs Predict How Discrete Worlds Change? Evaluates the ability of language models to predict state changes in discrete spatial grids across sequential operations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on spatial reasoning evaluation, not deployment safety.
MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support Presents an evaluation framework combining simulated mental health patients and expert-developed rubrics to assess conversational language models. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Deals solely with conversational text, not multimodal inputs.
MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support Presents an evaluation framework combining simulated mental health patients and expert-developed rubrics to assess conversational language models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not focus on model size, compression, or latency.
MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support Presents an evaluation framework combining simulated mental health patients and expert-developed rubrics to assess conversational language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not evaluate or develop retrieval-augmented generation architectures.
MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support Presents an evaluation framework combining simulated mental health patients and expert-developed rubrics to assess conversational language models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Uses simulated patients for evaluation rather than deploying autonomous agents.
MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support Presents an evaluation framework combining simulated mental health patients and expert-developed rubrics to assess conversational language models. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on mental health conversations, not software development.
Training Reasoning Models to Generate Diverse Scientific Ideas Introduces a set-level reinforcement learning objective to train language models to produce diverse and document-grounded scientific research ideas. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Does not address code writing or software engineering tasks.
Training Reasoning Models to Generate Diverse Scientific Ideas Introduces a set-level reinforcement learning objective to train language models to produce diverse and document-grounded scientific research ideas. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses primarily on training methods rather than general evaluation methodology.
Training Reasoning Models to Generate Diverse Scientific Ideas Introduces a set-level reinforcement learning objective to train language models to produce diverse and document-grounded scientific research ideas. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Exclusively processes textual scientific documents.
Training Reasoning Models to Generate Diverse Scientific Ideas Introduces a set-level reinforcement learning objective to train language models to produce diverse and document-grounded scientific research ideas. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Does not examine deployment safety, jailbreaks, or hallucinations.
Training Reasoning Models to Generate Diverse Scientific Ideas Introduces a set-level reinforcement learning objective to train language models to produce diverse and document-grounded scientific research ideas. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Investigates idea generation rather than multi-step tool-using agents.
Training Reasoning Models to Generate Diverse Scientific Ideas Introduces a set-level reinforcement learning objective to train language models to produce diverse and document-grounded scientific research ideas. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not explore efficiency, distillation, or edge deployment techniques.
OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling Proposes a visual verifier that decomposes complex prompts into sub-questions and assigns continuous scores to improve multimodal evaluation. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on multimodal verification methods, not autonomous agent workflows.
OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling Proposes a visual verifier that decomposes complex prompts into sub-questions and assigns continuous scores to improve multimodal evaluation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not address retrieval-augmented generation systems.
OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling Proposes a visual verifier that decomposes complex prompts into sub-questions and assigns continuous scores to improve multimodal evaluation. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not study model compression, quantization, or distillation.
OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling Proposes a visual verifier that decomposes complex prompts into sub-questions and assigns continuous scores to improve multimodal evaluation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Presents evaluation verification techniques, not safety alignment or guardrails.
OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling Proposes a visual verifier that decomposes complex prompts into sub-questions and assigns continuous scores to improve multimodal evaluation. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Not related to code generation or software engineering.
The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock Introduces a benchmark evaluating language model agents in real-time environments and a two-timescale training method to improve execution speed. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on latency and real-time execution, not safety or guardrails.
Evaluating Model Capabilities After Benchmark Saturation Tests whether transcripts of correctly solved tasks from saturated benchmarks can predict model performance on unseen harder evaluations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on post-saturation benchmark evaluation, not model efficiency or compression.
Evaluating Model Capabilities After Benchmark Saturation Tests whether transcripts of correctly solved tasks from saturated benchmarks can predict model performance on unseen harder evaluations. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies evaluation methodologies on saturated benchmarks rather than autonomous agent systems.
Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion The paper introduces a training-free schedule optimization method for accelerating sequential Monte Carlo guidance in discrete diffusion models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses diffusion sampling efficiency rather than AI deployment safety or guardrails.
Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion The paper introduces a training-free schedule optimization method for accelerating sequential Monte Carlo guidance in discrete diffusion models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents a diffusion sampling schedule rather than autonomous planning agents.
Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion The paper introduces a training-free schedule optimization method for accelerating sequential Monte Carlo guidance in discrete diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Develops a guidance algorithm rather than an LLM evaluation framework or benchmark.
AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks The paper introduces a modular measurement system to evaluate how agent frameworks, tools, and memory impact verifiable reasoning tasks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Evaluates token usage across agent harnesses, not model compression or efficient serving.
AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks The paper introduces a modular measurement system to evaluate how agent frameworks, tools, and memory impact verifiable reasoning tasks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on agent accuracy and harness benchmarking rather than safety, guardrails, or red-teaming.
When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus The paper demonstrates how correctness feedback undermines formal specification auditing and proposes a phase-separated alternative for Verus. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Examines specification auditing dynamics rather than general LLM evaluation benchmarks.
When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus The paper demonstrates how correctness feedback undermines formal specification auditing and proposes a phase-separated alternative for Verus. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies formal contract verification and auditing rather than autonomous agent systems.
Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models The paper proposes a selective safety framework for vision-language models using counterfactual pairs and representation density estimation. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on representation-level safety filtering for VLMs, not multi-step agent workflows.
Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models The paper proposes a selective safety framework for vision-language models using counterfactual pairs and representation density estimation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a safety defense mechanism rather than establishing a general evaluation benchmark.

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