Every paper checked — nothing hidden
All papers
Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.
All17,313In brief24More (on-topic)3,333Not relevant13,754Not assessed87Older352Could not confirm93Blogs with no paper266All blogs284
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| CLUE: Uncovering Social Context for Pragmatic Understanding Develops a two-stage training approach combining evidence reasoning and reinforcement learning to improve pragmatic social reasoning in conversational models. Preprint | AI safety and reliability in deployment via arxiv · 17 Oct, 06:17 | Not relevant Does not focus on safety, guardrails, or adversarial robustness. | |
| Lexical Reference Frames: Interpreting LM Residual Streams in Native Semantics Introduces lexical reference frames to interpret intermediate hidden states in language models using the model's own remaining layers. Preprint | LLMs for code via arxiv · 17 Oct, 06:17 | Not relevant Not related to code generation or software engineering. | |
| Lexical Reference Frames: Interpreting LM Residual Streams in Native Semantics Introduces lexical reference frames to interpret intermediate hidden states in language models using the model's own remaining layers. Preprint | Multimodal models via arxiv · 17 Oct, 06:17 | Not relevant Studies text-only transformer internal representations. | |
| Lexical Reference Frames: Interpreting LM Residual Streams in Native Semantics Introduces lexical reference frames to interpret intermediate hidden states in language models using the model's own remaining layers. Preprint | AI safety and reliability in deployment via arxiv · 17 Oct, 06:17 | Not relevant Presents mechanistic interpretability, not deployment safety or guardrails. | |
| Lexical Reference Frames: Interpreting LM Residual Streams in Native Semantics Introduces lexical reference frames to interpret intermediate hidden states in language models using the model's own remaining layers. Preprint | LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17 | Not relevant Focuses on mechanistic interpretability rather than evaluation benchmarks. | |
| Lexical Reference Frames: Interpreting LM Residual Streams in Native Semantics Introduces lexical reference frames to interpret intermediate hidden states in language models using the model's own remaining layers. Preprint | Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17 | Not relevant Does not relate to external document retrieval or RAG. | |
| Lexical Reference Frames: Interpreting LM Residual Streams in Native Semantics Introduces lexical reference frames to interpret intermediate hidden states in language models using the model's own remaining layers. Preprint | AI agents via arxiv · 17 Oct, 06:17 | Not relevant Studies internal residual stream interpretability, not agent workflows. | |
| 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. | |
| 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 | AI safety and reliability in deployment via arxiv · 17 Oct, 06:17 | Not relevant Does not address safety, jailbreaks, or deployment risks. | |
| 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 | LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17 | Not relevant Focuses on a captioning method rather than general benchmarking. | |
| 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 | Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17 | Not relevant Does not address retrieval-augmented generation systems. | |
| 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 | AI agents via arxiv · 17 Oct, 06:17 | Not relevant Focuses on audiovisual captioning, not agent planning or tool use. | |
| 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 | 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. | |
| 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. | |
| 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 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 | 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 | 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 | 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 | Small and efficient models via arxiv · 17 Oct, 06:17 | Not relevant Does not investigate model compression, distillation, or efficiency. | |
| 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. | |
| 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 | AI safety and reliability in deployment via arxiv · 17 Oct, 06:17 | Not relevant Focuses on spatial reasoning evaluation, not deployment safety. | |
| 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 | 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 | 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. | |
| 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 | 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 | 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. | |
| 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. | |
| 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 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 | 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 | Small and efficient models via arxiv · 17 Oct, 06:17 | Not relevant Does not explore efficiency, distillation, or edge deployment techniques. | |
| 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. | |
| 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 | 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 | 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 | LLMs for code via arxiv · 17 Oct, 06:17 | Not relevant Not related to code generation or software engineering. | |
| 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. | |
| 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. |
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