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 |
|---|---|---|---|
| Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders The paper proposes ReMaRk, a reinforcement learning method that trains recommender models using all available reader replies simultaneously to reduce variance. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Deals purely with text recommendation threads without multimodal components. | |
| Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders The paper proposes ReMaRk, a reinforcement learning method that trains recommender models using all available reader replies simultaneously to reduce variance. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on RL training optimization rather than evaluation methodology or benchmarks. | |
| Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders The paper proposes ReMaRk, a reinforcement learning method that trains recommender models using all available reader replies simultaneously to reduce variance. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on training efficiency for recommenders rather than safety guardrails or hallucination. | |
| Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders The paper proposes ReMaRk, a reinforcement learning method that trains recommender models using all available reader replies simultaneously to reduce variance. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on RL training recipes for recommenders rather than autonomous agents. | |
| Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders The paper proposes ReMaRk, a reinforcement learning method that trains recommender models using all available reader replies simultaneously to reduce variance. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Deals with recommendation reward functions rather than retrieval-augmented generation. | |
| Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders The paper proposes ReMaRk, a reinforcement learning method that trains recommender models using all available reader replies simultaneously to reduce variance. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Addresses recommendation systems, not program repair or code generation. | |
| Beyond Belief Nesting: Rethinking Higher-Order Theory of Mind in Large Language Models The paper introduces HopToM, a benchmark testing whether higher-order theory-of-mind tasks in language models genuinely evaluate nested reasoning rather than shortcuts. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not cover external knowledge retrieval or grounding. | |
| Beyond Belief Nesting: Rethinking Higher-Order Theory of Mind in Large Language Models The paper introduces HopToM, a benchmark testing whether higher-order theory-of-mind tasks in language models genuinely evaluate nested reasoning rather than shortcuts. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Evaluates text-based reasoning without multimodal data. | |
| Beyond Belief Nesting: Rethinking Higher-Order Theory of Mind in Large Language Models The paper introduces HopToM, a benchmark testing whether higher-order theory-of-mind tasks in language models genuinely evaluate nested reasoning rather than shortcuts. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on reasoning accuracy rather than model compression or inference efficiency. | |
| Beyond Belief Nesting: Rethinking Higher-Order Theory of Mind in Large Language Models The paper introduces HopToM, a benchmark testing whether higher-order theory-of-mind tasks in language models genuinely evaluate nested reasoning rather than shortcuts. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Examines narrative Theory of Mind reasoning, not code generation. | |
| Beyond Belief Nesting: Rethinking Higher-Order Theory of Mind in Large Language Models The paper introduces HopToM, a benchmark testing whether higher-order theory-of-mind tasks in language models genuinely evaluate nested reasoning rather than shortcuts. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on Theory of Mind reasoning rather than tool use or autonomous agents. | |
| Beyond Belief Nesting: Rethinking Higher-Order Theory of Mind in Large Language Models The paper introduces HopToM, a benchmark testing whether higher-order theory-of-mind tasks in language models genuinely evaluate nested reasoning rather than shortcuts. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Studies perspective reasoning capabilities rather than safety risks, jailbreaks, or guardrails. | |
| GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria The paper presents GENDIS, a multilingual dataset of YouTube comments from India, Kenya, and Nigeria annotated for gender-related discourse and stance. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not focus on model compression, quantization, or efficient architectures. | |
| GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria The paper presents GENDIS, a multilingual dataset of YouTube comments from India, Kenya, and Nigeria annotated for gender-related discourse and stance. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Deals with social media comments, not programming or code assistance. | |
| GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria The paper presents GENDIS, a multilingual dataset of YouTube comments from India, Kenya, and Nigeria annotated for gender-related discourse and stance. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Focuses on comment classification rather than retrieval-augmented generation. | |
| GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria The paper presents GENDIS, a multilingual dataset of YouTube comments from India, Kenya, and Nigeria annotated for gender-related discourse and stance. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Presents a discourse dataset without agent planning or tool use. | |
| GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria The paper presents GENDIS, a multilingual dataset of YouTube comments from India, Kenya, and Nigeria annotated for gender-related discourse and stance. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Analyzes text comments from YouTube without multimodal image or audio models. | |
| Full-duplex Spoken Language Modeling with Discrete Diffusion The paper presents Diffuplex, a full-duplex spoken language model that separates speech timing from content generation using discrete diffusion. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Focuses on spoken dialogue modeling rather than code generation. | |
| Full-duplex Spoken Language Modeling with Discrete Diffusion The paper presents Diffuplex, a full-duplex spoken language model that separates speech timing from content generation using discrete diffusion. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not involve external document retrieval or grounding. | |
| Full-duplex Spoken Language Modeling with Discrete Diffusion The paper presents Diffuplex, a full-duplex spoken language model that separates speech timing from content generation using discrete diffusion. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Presents a spoken language model rather than an evaluation benchmark. | |
| Full-duplex Spoken Language Modeling with Discrete Diffusion The paper presents Diffuplex, a full-duplex spoken language model that separates speech timing from content generation using discrete diffusion. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Proposes a speech model architecture rather than an autonomous tool-using agent. | |
| Full-duplex Spoken Language Modeling with Discrete Diffusion The paper presents Diffuplex, a full-duplex spoken language model that separates speech timing from content generation using discrete diffusion. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on latency and spoken dialogue architecture rather than safety or guardrails. | |
| Where Words Begin and End The paper evaluates audio forced aligners and fine-tunes Whisper to predict human-aligned word boundaries across read, conversational, and telephone speech. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Addresses acoustic-text alignment, not retrieval-augmented generation. | |
| Where Words Begin and End The paper evaluates audio forced aligners and fine-tunes Whisper to predict human-aligned word boundaries across read, conversational, and telephone speech. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Deals with speech-to-text alignment, not software engineering or code. | |
| Where Words Begin and End The paper evaluates audio forced aligners and fine-tunes Whisper to predict human-aligned word boundaries across read, conversational, and telephone speech. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not focus on model compression, quantization, or serving efficiency. | |
| Where Words Begin and End The paper evaluates audio forced aligners and fine-tunes Whisper to predict human-aligned word boundaries across read, conversational, and telephone speech. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Investigates word boundary timing accuracy rather than safety or reliability guardrails. | |
| Where Words Begin and End The paper evaluates audio forced aligners and fine-tunes Whisper to predict human-aligned word boundaries across read, conversational, and telephone speech. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on fine-tuning speech models rather than general LLM evaluation benchmarks. | |
| Where Words Begin and End The paper evaluates audio forced aligners and fine-tunes Whisper to predict human-aligned word boundaries across read, conversational, and telephone speech. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on forced speech alignment algorithms rather than agent workflows. | |
| BRIDGE: Selective Boundary Repair with Dependency Graph Evidence in Multi-Agent Systems The paper introduces BRIDGE, a runtime intervention framework that detects and repairs broken information handoffs across dependency graphs in multi-agent workflows. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Investigates multi-agent interaction traces without multimodal vision or audio data. | |
| BRIDGE: Selective Boundary Repair with Dependency Graph Evidence in Multi-Agent Systems The paper introduces BRIDGE, a runtime intervention framework that detects and repairs broken information handoffs across dependency graphs in multi-agent workflows. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Proposes a multi-agent repair framework rather than an evaluation benchmark. | |
| BRIDGE: Selective Boundary Repair with Dependency Graph Evidence in Multi-Agent Systems The paper introduces BRIDGE, a runtime intervention framework that detects and repairs broken information handoffs across dependency graphs in multi-agent workflows. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not address parameter reduction, distillation, or inference optimization. | |
| BRIDGE: Selective Boundary Repair with Dependency Graph Evidence in Multi-Agent Systems The paper introduces BRIDGE, a runtime intervention framework that detects and repairs broken information handoffs across dependency graphs in multi-agent workflows. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Addresses general multi-agent task collaboration rather than software engineering tasks. | |
| BRIDGE: Selective Boundary Repair with Dependency Graph Evidence in Multi-Agent Systems The paper introduces BRIDGE, a runtime intervention framework that detects and repairs broken information handoffs across dependency graphs in multi-agent workflows. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Focuses on multi-agent communication handoffs rather than external knowledge retrieval. | |
| Perturbation, Not Provenance: Why Activation Steering Does Not Make a Robust Text Watermark The paper tests activation steering as a text watermarking technique, finding that standard logit-based watermarking provides more robust detection against paraphrasing. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Focuses on watermark robustness without information retrieval components. | |
| Perturbation, Not Provenance: Why Activation Steering Does Not Make a Robust Text Watermark The paper tests activation steering as a text watermarking technique, finding that standard logit-based watermarking provides more robust detection against paraphrasing. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Investigates text watermarking techniques rather than multi-step agent systems. | |
| Perturbation, Not Provenance: Why Activation Steering Does Not Make a Robust Text Watermark The paper tests activation steering as a text watermarking technique, finding that standard logit-based watermarking provides more robust detection against paraphrasing. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on watermarking security rather than model efficiency or compression. | |
| Perturbation, Not Provenance: Why Activation Steering Does Not Make a Robust Text Watermark The paper tests activation steering as a text watermarking technique, finding that standard logit-based watermarking provides more robust detection against paraphrasing. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Deals exclusively with text representations and generation. | |
| Perturbation, Not Provenance: Why Activation Steering Does Not Make a Robust Text Watermark The paper tests activation steering as a text watermarking technique, finding that standard logit-based watermarking provides more robust detection against paraphrasing. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Evaluates watermark robustness rather than general LLM capability benchmarks. | |
| Perturbation, Not Provenance: Why Activation Steering Does Not Make a Robust Text Watermark The paper tests activation steering as a text watermarking technique, finding that standard logit-based watermarking provides more robust detection against paraphrasing. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Addresses general text watermarking, not code generation or program repair. | |
| Prefilling-dLLM v2: Learned Chunk Routing for Long-Context Diffusion Language Models The paper proposes a training approach that uses learned chunk routing to speed up long-context diffusion language model processing. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Focuses on internal long-context attention routing rather than external retrieval. | |
| Prefilling-dLLM v2: Learned Chunk Routing for Long-Context Diffusion Language Models The paper proposes a training approach that uses learned chunk routing to speed up long-context diffusion language model processing. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Addresses text-based diffusion language models without multimodal components. | |
| Prefilling-dLLM v2: Learned Chunk Routing for Long-Context Diffusion Language Models The paper proposes a training approach that uses learned chunk routing to speed up long-context diffusion language model processing. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on architectural efficiency rather than deployment safety or jailbreak prevention. | |
| Prefilling-dLLM v2: Learned Chunk Routing for Long-Context Diffusion Language Models The paper proposes a training approach that uses learned chunk routing to speed up long-context diffusion language model processing. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on attention routing architecture, not autonomous agent workflows. | |
| Prefilling-dLLM v2: Learned Chunk Routing for Long-Context Diffusion Language Models The paper proposes a training approach that uses learned chunk routing to speed up long-context diffusion language model processing. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Does not address program synthesis or code-specific language models. | |
| Prefilling-dLLM v2: Learned Chunk Routing for Long-Context Diffusion Language Models The paper proposes a training approach that uses learned chunk routing to speed up long-context diffusion language model processing. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Tests on RULER but primary contribution is an efficient context architecture. | |
| Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages The paper introduces Swiss4Arg, a multilingual argument mining dataset based on official Swiss voting brochures across four national languages. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Focuses on multilingual argument mining datasets rather than RAG systems. | |
| Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages The paper introduces Swiss4Arg, a multilingual argument mining dataset based on official Swiss voting brochures across four national languages. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not study model compression, quantization, or efficient inference. | |
| Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages The paper introduces Swiss4Arg, a multilingual argument mining dataset based on official Swiss voting brochures across four national languages. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Deals with political argument mining, not code generation or software engineering. | |
| Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages The paper introduces Swiss4Arg, a multilingual argument mining dataset based on official Swiss voting brochures across four national languages. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Presents an argument mining dataset without agent planning or tool use. | |
| Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages The paper introduces Swiss4Arg, a multilingual argument mining dataset based on official Swiss voting brochures across four national languages. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on linguistic argument extraction rather than safety or alignment guardrails. | |
| Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages The paper introduces Swiss4Arg, a multilingual argument mining dataset based on official Swiss voting brochures across four national languages. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Deals exclusively with multilingual text data without visual or audio modalities. | |
| Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation The paper evaluates whether providing page images to vision-language models improves the detection of errors in Japanese-English manga translation. Peer-reviewed | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Examines VLM evaluation judges rather than multi-step planning or tool-using agents. | |
| Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation The paper evaluates whether providing page images to vision-language models improves the detection of errors in Japanese-English manga translation. Peer-reviewed | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Studies manga translation, completely unrelated to industrial inspection or manufacturing defects. | |
| Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation The paper evaluates whether providing page images to vision-language models improves the detection of errors in Japanese-English manga translation. Peer-reviewed | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not involve retrieval-augmented generation or external document grounding. | |
| Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation The paper evaluates whether providing page images to vision-language models improves the detection of errors in Japanese-English manga translation. Peer-reviewed | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Focuses on manga translation evaluation, not software engineering or code. | |
| Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation The paper evaluates whether providing page images to vision-language models improves the detection of errors in Japanese-English manga translation. Peer-reviewed | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on translation evaluation accuracy rather than AI safety or reliability risks. | |
| Text-Only vs. Image-Aware VLM Judges for Manga Translation Evaluation The paper evaluates whether providing page images to vision-language models improves the detection of errors in Japanese-English manga translation. Peer-reviewed | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not address model compression, quantization, distillation, or serving efficiency. | |
| Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language Models The paper proposes Batch-Paced, a method that dynamically allocates decoding compute across batched requests to improve discrete diffusion language model inference. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Focuses on general text decoding rather than software engineering code generation. | |
| Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language Models The paper proposes Batch-Paced, a method that dynamically allocates decoding compute across batched requests to improve discrete diffusion language model inference. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Addresses batch decoding in diffusion models without external knowledge retrieval. | |
| Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language Models The paper proposes Batch-Paced, a method that dynamically allocates decoding compute across batched requests to improve discrete diffusion language model inference. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Evaluates on benchmarks but main contribution is an efficient serving algorithm. |
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