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 |
|---|---|---|---|
| A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading Provides a tutorial analyzing theoretical expressiveness gains and parameter tradeoffs when re-uploading data in quantum amplitude embedding architectures. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Provides a theoretical tutorial on quantum embeddings, not LLM benchmarking. | |
| A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading Provides a tutorial analyzing theoretical expressiveness gains and parameter tradeoffs when re-uploading data in quantum amplitude embedding architectures. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Discusses quantum machine learning encoding, not classical small or efficient LLMs. | |
| A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading Provides a tutorial analyzing theoretical expressiveness gains and parameter tradeoffs when re-uploading data in quantum amplitude embedding architectures. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Presents a tutorial on quantum amplitude embeddings, unrelated to AI agents. | |
| Learning Adaptive Deliberation Control for Reliable Recognition with Multi-Agent Language Models Presents ADMA, an adaptive deliberation controller that dynamically adjusts multi-agent critique and coordination to improve reasoning accuracy. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on an adaptive multi-agent coordination system rather than benchmark design. | |
| FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Addresses e-commerce catalog search, not industrial manufacturing defect detection. | |
| FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Deals with product search evaluation, not AI safety or alignment. | |
| FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Covers fashion search retrieval, not autonomous planning agents. | |
| FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Uses vector quantization for clustering, not efficient model deployment or inference. | |
| FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on catalog retrieval evaluation without multimodal integration as main focus. | |
| MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision Releases MMTA, a multimodal benchmark testing the ability of language models to jointly analyze time series, text, and visual data. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on benchmarking multimodal perception, not safety or guardrails. | |
| MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision Releases MMTA, a multimodal benchmark testing the ability of language models to jointly analyze time series, text, and visual data. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Evaluates multimodal model perception on time series, not tool-using agents. | |
| MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision Releases MMTA, a multimodal benchmark testing the ability of language models to jointly analyze time series, text, and visual data. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Presents an evaluation benchmark rather than efficient model compression. | |
| Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills Introduces Socratic Skill Learning, interleaving expert guidance with student practice to produce reusable and transferable agent skills across tasks. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on agent skill acquisition, not safety guardrails or red-teaming. | |
| TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting Proposes TStruct, a network that captures recurring temporal relationship structures across variables to improve long-term time-series forecasting. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Proposes a forecasting model architecture, not an evaluation benchmark. | |
| TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting Proposes TStruct, a network that captures recurring temporal relationship structures across variables to improve long-term time-series forecasting. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Addresses standard time series forecasting, not AI safety or robustness. | |
| TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting Proposes TStruct, a network that captures recurring temporal relationship structures across variables to improve long-term time-series forecasting. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on temporal modeling architectures, not model compression or serving. | |
| TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting Proposes TStruct, a network that captures recurring temporal relationship structures across variables to improve long-term time-series forecasting. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Deals with time-series forecasting, not LLM agents or planning. | |
| RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions Presents RT-J, a relational transformer pretrained on diverse databases to achieve strong in-context table predictions with few sample examples. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Introduces a model architecture and pretraining method, not an evaluation standard. | |
| RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions Presents RT-J, a relational transformer pretrained on diverse databases to achieve strong in-context table predictions with few sample examples. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on relational data prediction, not safety or alignment. | |
| RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions Presents RT-J, a relational transformer pretrained on diverse databases to achieve strong in-context table predictions with few sample examples. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Presents a tabular foundation model, not an autonomous agent system. | |
| ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning Presents ECMR, a dynamic evidence-aware controller that guides multi-agent deliberation steps to prevent false consensus and reduce redundant reasoning. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Proposes a multi-agent control mechanism rather than an evaluation benchmark. | |
| Cross-Lingual Transfer of Evil Vectors Is Directionally Asymmetric Shows that evil persona vectors extracted from low-resource languages transfer poorly and fail to steer model behavior effectively across languages. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Focuses on residual stream steering, not retrieval-augmented generation. | |
| Cross-Lingual Transfer of Evil Vectors Is Directionally Asymmetric Shows that evil persona vectors extracted from low-resource languages transfer poorly and fail to steer model behavior effectively across languages. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Explores activation steering, not model compression or efficiency. | |
| Cross-Lingual Transfer of Evil Vectors Is Directionally Asymmetric Shows that evil persona vectors extracted from low-resource languages transfer poorly and fail to steer model behavior effectively across languages. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on activation vector steering rather than evaluation benchmark design. | |
| Cross-Lingual Transfer of Evil Vectors Is Directionally Asymmetric Shows that evil persona vectors extracted from low-resource languages transfer poorly and fail to steer model behavior effectively across languages. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Studies persona activation vectors, not multi-step agent planning. | |
| Cross-Lingual Transfer of Evil Vectors Is Directionally Asymmetric Shows that evil persona vectors extracted from low-resource languages transfer poorly and fail to steer model behavior effectively across languages. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Does not address code generation or software engineering tasks. | |
| Cross-Lingual Transfer of Evil Vectors Is Directionally Asymmetric Shows that evil persona vectors extracted from low-resource languages transfer poorly and fail to steer model behavior effectively across languages. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on text representations across languages, not vision or audio. | |
| Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards Proposes CoSIR to train multi-agent collaboration policies using information-theoretic surprisal rewards that assess the helpfulness of communication. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Investigates text-based collaboration, not multimodal inputs. | |
| Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards Proposes CoSIR to train multi-agent collaboration policies using information-theoretic surprisal rewards that assess the helpfulness of communication. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on cooperative agent training, not inference acceleration or quantization. | |
| Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards Proposes CoSIR to train multi-agent collaboration policies using information-theoretic surprisal rewards that assess the helpfulness of communication. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on task collaboration strategies rather than deployment safety or jailbreaks. | |
| Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards Proposes CoSIR to train multi-agent collaboration policies using information-theoretic surprisal rewards that assess the helpfulness of communication. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Focuses on multi-agent collaboration, not code generation or software engineering. | |
| Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards Proposes CoSIR to train multi-agent collaboration policies using information-theoretic surprisal rewards that assess the helpfulness of communication. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Deals with inter-agent communication, not document database retrieval. | |
| Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards Proposes CoSIR to train multi-agent collaboration policies using information-theoretic surprisal rewards that assess the helpfulness of communication. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Proposes a multi-agent training framework, not an evaluation benchmark. | |
| Interaction-as-a-Judge Presents Interaction-as-a-Judge, using computer-use agents to evaluate interactive web and desktop software applications through direct dynamic interaction. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Focuses on agentic evaluation methods, not model compression or efficiency. | |
| Interaction-as-a-Judge Presents Interaction-as-a-Judge, using computer-use agents to evaluate interactive web and desktop software applications through direct dynamic interaction. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on preference evaluation of software rather than safety vulnerabilities. | |
| Evaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features Evaluates SHAP attribution methods on sparse autoencoder features, finding weak correspondence with direct causal feature intervention in language models. Preprint | LLMs for code via arxiv · 15 Oct, 06:17 | Not relevant Does not address code generation or software engineering. | |
| Evaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features Evaluates SHAP attribution methods on sparse autoencoder features, finding weak correspondence with direct causal feature intervention in language models. Preprint | Small and efficient models via arxiv · 15 Oct, 06:17 | Not relevant Studies feature interpretability rather than model compression or efficient serving. | |
| Evaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features Evaluates SHAP attribution methods on sparse autoencoder features, finding weak correspondence with direct causal feature intervention in language models. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on mechanistic interpretability of SAEs, not autonomous agents. | |
| Evaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features Evaluates SHAP attribution methods on sparse autoencoder features, finding weak correspondence with direct causal feature intervention in language models. Preprint | Multimodal models via arxiv · 15 Oct, 06:17 | Not relevant Evaluates language model internal representations, not multimodal models. | |
| Evaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features Evaluates SHAP attribution methods on sparse autoencoder features, finding weak correspondence with direct causal feature intervention in language models. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17 | Not relevant Does not involve external document retrieval or grounding. | |
| PaceReasoner: Label-Free Learning to Answer Briefly from Long Reasoning Introduces PaceReasoner, using long reasoning outputs during test-time reinforcement learning to boost the accuracy of brief, low-latency model answers. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Deals with reasoning token efficiency, not safety or guardrails. | |
| PaceReasoner: Label-Free Learning to Answer Briefly from Long Reasoning Introduces PaceReasoner, using long reasoning outputs during test-time reinforcement learning to boost the accuracy of brief, low-latency model answers. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Introduces a test-time RL method, not a benchmark or evaluation protocol. | |
| PaceReasoner: Label-Free Learning to Answer Briefly from Long Reasoning Introduces PaceReasoner, using long reasoning outputs during test-time reinforcement learning to boost the accuracy of brief, low-latency model answers. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on reasoning distillation and length control, not tool use or agents. | |
| Visual Dependence Does Not Imply Answerability: A Systematic Analysis of Answerability Signals in Frozen InstructBLIP Analyzes answerability signals in frozen visual question answering models, finding standard confidence and alignment metrics fail to reliably detect unanswerable inputs. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Analyzes question-answering behavior in VLMs, not agentic planning systems. | |
| Visual Dependence Does Not Imply Answerability: A Systematic Analysis of Answerability Signals in Frozen InstructBLIP Analyzes answerability signals in frozen visual question answering models, finding standard confidence and alignment metrics fail to reliably detect unanswerable inputs. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Studies visual question answerability, not industrial defect detection. | |
| DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving Introduces DriveWLA, a mixture-of-transformers architecture unifying scene semantics, future dynamic prediction, and vehicle trajectory planning. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Applies to autonomous vehicles rather than manufacturing quality control or industrial inspection. | |
| DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving Introduces DriveWLA, a mixture-of-transformers architecture unifying scene semantics, future dynamic prediction, and vehicle trajectory planning. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Proposes an autonomous driving architecture, not an evaluation benchmark. | |
| DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving Introduces DriveWLA, a mixture-of-transformers architecture unifying scene semantics, future dynamic prediction, and vehicle trajectory planning. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Deals with autonomous driving trajectory planning, not general LLM tool-use agents. | |
| DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving Introduces DriveWLA, a mixture-of-transformers architecture unifying scene semantics, future dynamic prediction, and vehicle trajectory planning. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on trajectory prediction performance, not safety guardrails or alignment. | |
| SpecCloak: Probing Harmful Code Generation through Engineering Specifications Finds that framing malicious coding prompts as engineering specifications systematically reduces language model refusal rates across multiple models. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on safety probing mechanisms rather than benchmark design. | |
| SpecCloak: Probing Harmful Code Generation through Engineering Specifications Finds that framing malicious coding prompts as engineering specifications systematically reduces language model refusal rates across multiple models. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Studies refusal mechanisms in code generation, not agent planning workflows. | |
| LinMorph: Learning to Morph Weights for Efficient Video DiT Linearization Proposes LinMorph, a learnable weight transformation method to initialize linear attention models from pretrained video diffusion transformers efficiently. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Introduces a weight morphing method, not a general evaluation framework. | |
| LinMorph: Learning to Morph Weights for Efficient Video DiT Linearization Proposes LinMorph, a learnable weight transformation method to initialize linear attention models from pretrained video diffusion transformers efficiently. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Covers diffusion transformer linearization, not tool-using or planning agents. | |
| LinMorph: Learning to Morph Weights for Efficient Video DiT Linearization Proposes LinMorph, a learnable weight transformation method to initialize linear attention models from pretrained video diffusion transformers efficiently. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Addresses model acceleration, not safety, hallucination, or red teaming. | |
| LinMorph: Learning to Morph Weights for Efficient Video DiT Linearization Proposes LinMorph, a learnable weight transformation method to initialize linear attention models from pretrained video diffusion transformers efficiently. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Focuses on generative video transformer architecture, not industrial inspection. | |
| CineMem: Attention-Aligned Fine-grained Memory Retrieval for Multi-Shot Long Video Generation Introduces CineMem, an attention-aligned memory retrieval framework using clustered KV caches for autoregressive multi-shot long video generation. Preprint | AI agents via arxiv · 15 Oct, 06:17 | Not relevant Focuses on video generation cache retrieval, not autonomous planning agents. | |
| CineMem: Attention-Aligned Fine-grained Memory Retrieval for Multi-Shot Long Video Generation Introduces CineMem, an attention-aligned memory retrieval framework using clustered KV caches for autoregressive multi-shot long video generation. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:17 | Not relevant Focuses on video synthesis efficiency, not AI safety or deployment guardrails. | |
| CineMem: Attention-Aligned Fine-grained Memory Retrieval for Multi-Shot Long Video Generation Introduces CineMem, an attention-aligned memory retrieval framework using clustered KV caches for autoregressive multi-shot long video generation. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Proposes a generation architecture, not an evaluation benchmark or methodology. | |
| CineMem: Attention-Aligned Fine-grained Memory Retrieval for Multi-Shot Long Video Generation Introduces CineMem, an attention-aligned memory retrieval framework using clustered KV caches for autoregressive multi-shot long video generation. Preprint | Computer vision for industrial applications via arxiv · 15 Oct, 06:17 | Not relevant Deals with generative video synthesis, not industrial inspection or defect detection. | |
| GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation Presents a memory decomposition framework separating spatio-temporal beliefs to support autonomous robot navigation under evolving online goals. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17 | Not relevant Focuses on robot navigation frameworks, not LLM evaluation benchmarks. |
13,681–13,740 of 17,313← PreviousNext →