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 |
|---|---|---|---|
| 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 | 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 | 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 | 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 | 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 | 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. | |
| 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 | 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 | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Deals with political argument mining, not code generation or software engineering. | |
| 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. | |
| 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 | 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 | 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 | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Studies manga translation, completely unrelated to industrial inspection or manufacturing defects. | |
| 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 | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Addresses inference efficiency rather than safety, guardrails, or jailbreak prevention. | |
| 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 | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Studies discrete diffusion language models without multimodal inputs or outputs. | |
| 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. | |
| 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 | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on diffusion decoding algorithms rather than multi-step autonomous agent systems. | |
| Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not address model compression, pruning, or serving efficiency. | |
| Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not focus on retrieval-augmented generation methods. | |
| Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on language agents without multimodal data. | |
| Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Not specific to software engineering or code generation. | |
| Agent-Reflex: Teaching Language Agents to Act Reflectively The paper introduces Agent-Reflex, a training framework designed to teach language agents how to reflect and recover from noisy environment feedback. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Presents a training framework rather than evaluation benchmarks. | |
| Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Does not focus on deployment safety, jailbreaks, or guardrails. | |
| Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Presents a large 35B model, not model compression or small models. | |
| Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Does not cover software engineering or code generation. | |
| Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Presents a model technical report rather than evaluation benchmarks. | |
| Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not involve retrieval-augmented generation architectures. | |
| Fish Audio Drama 3 Technical Report The paper introduces Fish Audio Drama 3, an open-source text-to-speech model designed for multi-character dialogue, long-form synthesis, and iterative voice revision. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Does not focus on autonomous planning, agents, or tool use. | |
| Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not cover retrieval-augmented generation. | |
| Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Does not study multi-step agents, planning, or tool use. | |
| Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Analyzes text essay grading, not multimodal models. | |
| Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Does not address coding or software engineering. | |
| Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not address model compression or inference efficiency. | |
| A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Deals solely with text dialogue without multimodal data. | |
| A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Does not address code generation or software engineering. | |
| A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on dialogue comprehension, not safety or reliability risks. | |
| A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not cover model compression, distillation, or efficient inference. | |
| A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Presents a language-specific dialogue dataset rather than broader LLM evaluation. | |
| A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not involve retrieval-augmented generation. | |
| A Benchmark for Bangla Task-Oriented Dialogue Understanding The paper introduces BanglaTOD, a tri-lingual benchmark for task-oriented dialogue understanding covering Bangla, English, and romanized Banglish across nine service domains. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on dialogue state tracking rather than autonomous agent planning. | |
| From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not address retrieval-augmented generation methods. | |
| From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on task collaboration performance rather than safety guardrails. | |
| From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not cover model compression or inference efficiency. | |
| From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Not specific to coding or software engineering agents. | |
| From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on optimization methods rather than benchmark design. | |
| From Agents to Edges: Paired Optimization for Multi-Agent Collaboration The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on text-based multi-agent collaboration, not multimodal models. | |
| Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Does not study model compression, distillation, or serving. | |
| Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Probes internal representations rather than deployment safety or jailbreaks. | |
| Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Does not focus on code generation or software engineering. | |
| Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Exclusively covers text story generation, not multimodal inputs. | |
| Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Does not focus on benchmarking or LLM evaluation frameworks. | |
| Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not involve retrieval-augmented generation methods. | |
| Narrative Lookahead in LLMs: Event-Level Decodability, Predictive Advantage, and Steering The paper probes language model hidden states across generated stories to test whether models anticipate narrative events multiple steps ahead. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Investigates text generation dynamics, not multi-step agents or tools. | |
| Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges The paper evaluates typed decision models against standard language model judges to measure how emotional tone and biases affect their evaluation accuracy. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not address retrieval-augmented generation systems. |
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