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 |
|---|---|---|---|
| Simple Linear Corrections for Compression in Late Interaction The paper proposes a linear score correction method to recover retrieval quality in compressed late-interaction document representations. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Evaluates visual retrieval benchmarks but core contribution is text/multi-vector compression. | |
| Simple Linear Corrections for Compression in Late Interaction The paper proposes a linear score correction method to recover retrieval quality in compressed late-interaction document representations. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on vector compression in retrieval, not multi-step agent planning or tools. | |
| Simple Linear Corrections for Compression in Late Interaction The paper proposes a linear score correction method to recover retrieval quality in compressed late-interaction document representations. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on information retrieval scoring, not programming or code generation. | |
| Simple Linear Corrections for Compression in Late Interaction The paper proposes a linear score correction method to recover retrieval quality in compressed late-interaction document representations. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Deals with retrieval scoring efficiency, not AI safety, hallucination, or alignment. | |
| Modeling the Arrival Time of Information-Bearing Events in Natural Language The paper uses neural temporal point processes to model the timing of information-bearing events in human and model generated text. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Examines temporal point processes, not efficient model architectures. | |
| Modeling the Arrival Time of Information-Bearing Events in Natural Language The paper uses neural temporal point processes to model the timing of information-bearing events in human and model generated text. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Analyzes linguistic event timing, not agent tool execution. | |
| Modeling the Arrival Time of Information-Bearing Events in Natural Language The paper uses neural temporal point processes to model the timing of information-bearing events in human and model generated text. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Analyzes token arrival statistics, not an evaluation benchmark. | |
| Modeling the Arrival Time of Information-Bearing Events in Natural Language The paper uses neural temporal point processes to model the timing of information-bearing events in human and model generated text. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Analyzes language generation patterns without addressing deployment safety. | |
| Modeling the Arrival Time of Information-Bearing Events in Natural Language The paper uses neural temporal point processes to model the timing of information-bearing events in human and model generated text. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Studies statistical text generation, not retrieval-augmented systems. | |
| Modeling the Arrival Time of Information-Bearing Events in Natural Language The paper uses neural temporal point processes to model the timing of information-bearing events in human and model generated text. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Investigates natural language generation properties, not software engineering. | |
| Modeling the Arrival Time of Information-Bearing Events in Natural Language The paper uses neural temporal point processes to model the timing of information-bearing events in human and model generated text. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Studies natural language text streams without multimodal inputs. | |
| The Thinking Leakage in Reinforcement Learning for LLMs The paper examines whether non-thinking mode reinforcement learning gains in hybrid reasoning models stem from leakage of thinking-mode activations. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Examines reasoning modes in language models, not multimodal tasks. | |
| The Thinking Leakage in Reinforcement Learning for LLMs The paper examines whether non-thinking mode reinforcement learning gains in hybrid reasoning models stem from leakage of thinking-mode activations. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Studies reasoning mechanics without retrieval-augmented generation. | |
| The Thinking Leakage in Reinforcement Learning for LLMs The paper examines whether non-thinking mode reinforcement learning gains in hybrid reasoning models stem from leakage of thinking-mode activations. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Analyzes internal reasoning mechanisms, not model efficiency. | |
| The Thinking Leakage in Reinforcement Learning for LLMs The paper examines whether non-thinking mode reinforcement learning gains in hybrid reasoning models stem from leakage of thinking-mode activations. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Analyzes general reasoning models rather than programming tools. | |
| The Thinking Leakage in Reinforcement Learning for LLMs The paper examines whether non-thinking mode reinforcement learning gains in hybrid reasoning models stem from leakage of thinking-mode activations. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Investigates internal reasoning modes, not tool-using agents. | |
| ASSET: Adaptive Speech Segmentation via Reinforcement Learning The paper introduces ASSET, a reinforcement learning method that adaptively compresses speech encoder outputs into continuous audio tokens. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on speech segmentation architectures, not agent workflows. | |
| ASSET: Adaptive Speech Segmentation via Reinforcement Learning The paper introduces ASSET, a reinforcement learning method that adaptively compresses speech encoder outputs into continuous audio tokens. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Focuses on audio token compression rather than safety risks. | |
| ASSET: Adaptive Speech Segmentation via Reinforcement Learning The paper introduces ASSET, a reinforcement learning method that adaptively compresses speech encoder outputs into continuous audio tokens. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Presents an audio compression method, not an evaluation benchmark. | |
| ASSET: Adaptive Speech Segmentation via Reinforcement Learning The paper introduces ASSET, a reinforcement learning method that adaptively compresses speech encoder outputs into continuous audio tokens. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Deals with audio token compression, not retrieval augmentation. | |
| ASSET: Adaptive Speech Segmentation via Reinforcement Learning The paper introduces ASSET, a reinforcement learning method that adaptively compresses speech encoder outputs into continuous audio tokens. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Addresses speech encoding, not software code generation. | |
| Evaluating LLMs as Agentic Workflow Executors in Real-World Customer Support Skills The paper introduces a benchmark derived from real customer support dialogues to evaluate language models executing bounded, tool-using skills. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Studies customer service text dialogues without multimodal models. | |
| Evaluating LLMs as Agentic Workflow Executors in Real-World Customer Support Skills The paper introduces a benchmark derived from real customer support dialogues to evaluate language models executing bounded, tool-using skills. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Evaluates off-the-shelf models rather than compression techniques. | |
| Evaluating LLMs as Agentic Workflow Executors in Real-World Customer Support Skills The paper introduces a benchmark derived from real customer support dialogues to evaluate language models executing bounded, tool-using skills. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Evaluates customer support dialogues, not programming assistants. | |
| Evaluating LLMs as Agentic Workflow Executors in Real-World Customer Support Skills The paper introduces a benchmark derived from real customer support dialogues to evaluate language models executing bounded, tool-using skills. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on customer support tool workflows, not retrieval systems. | |
| Multimodal Co-Generation With Continuous Bitstream Diffusion The paper applies continuous bitstream diffusion to joint and conditional generation across multimodal data without learned tokenizers. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Explores multimodal generation architectures without addressing safety. | |
| Multimodal Co-Generation With Continuous Bitstream Diffusion The paper applies continuous bitstream diffusion to joint and conditional generation across multimodal data without learned tokenizers. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Studies generative diffusion architectures, not model efficiency. | |
| Multimodal Co-Generation With Continuous Bitstream Diffusion The paper applies continuous bitstream diffusion to joint and conditional generation across multimodal data without learned tokenizers. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Introduces a generative model framework, not an evaluation benchmark. | |
| Multimodal Co-Generation With Continuous Bitstream Diffusion The paper applies continuous bitstream diffusion to joint and conditional generation across multimodal data without learned tokenizers. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on multimodal generative diffusion, not software coding. | |
| Multimodal Co-Generation With Continuous Bitstream Diffusion The paper applies continuous bitstream diffusion to joint and conditional generation across multimodal data without learned tokenizers. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Presents generative diffusion modeling without retrieval augmentation. | |
| Multimodal Co-Generation With Continuous Bitstream Diffusion The paper applies continuous bitstream diffusion to joint and conditional generation across multimodal data without learned tokenizers. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on continuous diffusion modeling, not agent workflows. | |
| Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Presents a text watermarking algorithm, not retrieval systems. | |
| Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Studies watermarking robustness, not model pruning or distillation. | |
| Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on generation watermarking, not agent tool execution. | |
| Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Introduces a watermarking method rather than an evaluation benchmark. | |
| Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Addresses text provenance watermarking, not code generation. | |
| Tokenization-Invariant Watermarking: A Byte-Local Method for LLM Text The paper presents a byte-local text watermarking method that scores outputs without invoking tokenizers during detection. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Applies watermarking to text bytes without multimodal elements. | |
| SciCurateBench: Curating Experimental Data from Heterogeneous Scientific Literature with Verbal-Action Agents The paper presents SciCurateBench to evaluate language models on verifying complex experimental conditions from unprocessed scientific literature. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Benchmarking curation accuracy without addressing safety or reliability. | |
| SciCurateBench: Curating Experimental Data from Heterogeneous Scientific Literature with Verbal-Action Agents The paper presents SciCurateBench to evaluate language models on verifying complex experimental conditions from unprocessed scientific literature. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Examines complex multi-agent curation, not efficient small models. | |
| SciCurateBench: Curating Experimental Data from Heterogeneous Scientific Literature with Verbal-Action Agents The paper presents SciCurateBench to evaluate language models on verifying complex experimental conditions from unprocessed scientific literature. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on scientific literature curation rather than software engineering. | |
| SciCurateBench: Curating Experimental Data from Heterogeneous Scientific Literature with Verbal-Action Agents The paper presents SciCurateBench to evaluate language models on verifying complex experimental conditions from unprocessed scientific literature. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Processes text and document criteria without multimodal modeling. | |
| SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering The paper presents SPROUT, a snapshot-based rollout framework to scale reinforcement learning for long-horizon software engineering tasks. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Targets software engineering code environments without multimodality. | |
| SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering The paper presents SPROUT, a snapshot-based rollout framework to scale reinforcement learning for long-horizon software engineering tasks. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Introduces a training framework rather than an evaluation benchmark. | |
| SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering The paper presents SPROUT, a snapshot-based rollout framework to scale reinforcement learning for long-horizon software engineering tasks. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on agent rollback mechanisms, not retrieval augmentation. | |
| SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering The paper presents SPROUT, a snapshot-based rollout framework to scale reinforcement learning for long-horizon software engineering tasks. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Optimizes agent rollout efficiency rather than safety or guardrails. | |
| SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering The paper presents SPROUT, a snapshot-based rollout framework to scale reinforcement learning for long-horizon software engineering tasks. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on agent rollout frameworks, not model compression. | |
| Scaling Laws for Multimodal Data Mixtures The paper derives multimodal data mixing scaling laws across text, vision, and audio using mixture-of-experts models. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Studies pretraining mixture scaling, not retrieval-augmented pipelines. | |
| Scaling Laws for Multimodal Data Mixtures The paper derives multimodal data mixing scaling laws across text, vision, and audio using mixture-of-experts models. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Studies pretraining data scaling laws rather than inference efficiency. | |
| Scaling Laws for Multimodal Data Mixtures The paper derives multimodal data mixing scaling laws across text, vision, and audio using mixture-of-experts models. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Analyzes multimodal pretraining data mixtures, not autonomous agents. | |
| Scaling Laws for Multimodal Data Mixtures The paper derives multimodal data mixing scaling laws across text, vision, and audio using mixture-of-experts models. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on multimodal data scaling, not code generation. | |
| Scaling Laws for Multimodal Data Mixtures The paper derives multimodal data mixing scaling laws across text, vision, and audio using mixture-of-experts models. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Derives scaling laws for training rather than benchmarking systems. | |
| Scaling Laws for Multimodal Data Mixtures The paper derives multimodal data mixing scaling laws across text, vision, and audio using mixture-of-experts models. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Studies compute optima for pretraining rather than safety risks. | |
| MemForm: Benchmarking Memory Formation Poisoning in LLM Agents The paper introduces MemForm to benchmark adversarial memory formation poisoning across five input interfaces in language model agents. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Investigates text-based agent memory without multimodal components. | |
| MemForm: Benchmarking Memory Formation Poisoning in LLM Agents The paper introduces MemForm to benchmark adversarial memory formation poisoning across five input interfaces in language model agents. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on agent memory formation poisoning, not retrieval architectures. | |
| MemForm: Benchmarking Memory Formation Poisoning in LLM Agents The paper introduces MemForm to benchmark adversarial memory formation poisoning across five input interfaces in language model agents. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Addresses adversarial memory poisoning, not model compression. | |
| MemForm: Benchmarking Memory Formation Poisoning in LLM Agents The paper introduces MemForm to benchmark adversarial memory formation poisoning across five input interfaces in language model agents. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies agent memory vulnerabilities, not software coding. | |
| Neither True nor False: Do LLMs Represent Truth-Value Gaps in Plural Predication? The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Examines natural language semantics, not software engineering models. | |
| Neither True nor False: Do LLMs Represent Truth-Value Gaps in Plural Predication? The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Investigates theoretical semantics, not operational safety or reliability. | |
| Neither True nor False: Do LLMs Represent Truth-Value Gaps in Plural Predication? The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Studies linguistic truth-value gaps without external document retrieval. | |
| Neither True nor False: Do LLMs Represent Truth-Value Gaps in Plural Predication? The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on semantic formal logic, not agent planning or tools. |
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