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 |
|---|---|---|---|
| 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. | |
| 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 | 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 | 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 | 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 | 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 | 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 | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Studies compute optima for pretraining rather than safety risks. | |
| 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 | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Analyzes multimodal pretraining data mixtures, not autonomous agents. | |
| 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 | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies agent memory vulnerabilities, not software coding. | |
| 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 | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on agent memory formation poisoning, not retrieval architectures. | |
| 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. | |
| 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 | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Analyzes natural language sentences without multimodal capabilities. | |
| 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 | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on semantic reasoning limitations, not model efficiency. | |
| 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. | |
| Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Covers text gloss pairs across languages without multimodal components. | |
| Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Tests lexical semantic consistency, not programming assistants. | |
| Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Evaluates semantic relation knowledge, not tool-using agents. | |
| Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Studies relation prediction, not retrieval from external sources. | |
| Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on semantic evaluation rather than efficient architectures. | |
| Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs The paper introduces a bidirectional gloss-pair prediction task to evaluate semantic relation knowledge across multiple languages in language models. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Examines lexical semantic consistency rather than safety risks. | |
| Re-executing Coding-Agent Evaluations: Unchanged Decisions and Unresolved Policy Risk The paper re-evaluates coding-agent benchmark results to measure sensitivity to test execution environments and grading configurations. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Studies coding benchmarks without addressing retrieval-augmented generation. | |
| Re-executing Coding-Agent Evaluations: Unchanged Decisions and Unresolved Policy Risk The paper re-evaluates coding-agent benchmark results to measure sensitivity to test execution environments and grading configurations. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on benchmark reproducibility, not small or efficient models. | |
| Re-executing Coding-Agent Evaluations: Unchanged Decisions and Unresolved Policy Risk The paper re-evaluates coding-agent benchmark results to measure sensitivity to test execution environments and grading configurations. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Pertains strictly to code and execution environments, not multimodality. | |
| Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Studies content moderation tasks, not code generation or repair. | |
| Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Analyzes text classification registers without multimodal inputs. | |
| Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on toxic content classification, not document retrieval systems. | |
| Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Assesses model classification under irony, not autonomous agent planning. | |
| Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks This paper audits Chinese language models for bias and error propagation across formal and ironic social registers. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Investigates evaluation validity, not model compression or efficiency. | |
| EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Examines inference compression techniques, not safety or guardrails. | |
| EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Studies KV-cache quantization, not retrieval-augmented generation systems. | |
| EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Examines KV-cache compression on hardware, not agent workflows. | |
| EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Analyzes on-device model compression, not software coding tools. | |
| EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model This paper audits key-cache compression and weight pruning techniques on a small edge-deployed language model testbed. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Investigates text language model caches, not multimodal systems. | |
| Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on general inference steering rather than coding tasks. | |
| Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Studies text language models, not multimodal inputs. | |
| Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Investigates internal neuron steering, not autonomous tool-using agents. | |
| Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Explores internal model steering without external knowledge retrieval. | |
| Query-Adaptive Neuron Steering for LLMs This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Proposes an inference-time steering mechanism, not an evaluation suite. | |
| LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Focuses on historical document comprehension, not programming tasks. | |
| LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Evaluates document question answering rather than multi-step tool agents. | |
| LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Analyzes benchmark performance rather than model efficiency or compression. | |
| LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Assesses document QA accuracy without addressing safety risks. | |
| Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint | AI safety and reliability in deployment via arxiv · 15 Oct, 06:18 | Not relevant Targets distillation for reasoning performance rather than safety guardrails. | |
| Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Operates entirely on text-based math reasoning without multimodality. | |
| Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Presents a distillation training method, not an evaluation framework. | |
| Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18 | Not relevant Focuses on distillation training, not retrieval-augmented generation systems. | |
| Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint | LLMs for code via arxiv · 15 Oct, 06:18 | Not relevant Evaluates math reasoning benchmarks, not software engineering or code. | |
| Reward-Aligned Distillation This paper presents a bilevel distillation method that weights teacher supervision tokens based on their predicted impact on student reward. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Deals with training distillation algorithms, not interactive agents. | |
| Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint | AI agents via arxiv · 15 Oct, 06:18 | Not relevant Focuses on preference optimization objectives, not agent planning or tools. | |
| Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint | LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18 | Not relevant Proposes a training optimization method rather than evaluation benchmarks. | |
| Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint | Multimodal models via arxiv · 15 Oct, 06:18 | Not relevant Studies text alignment without multimodal inputs or outputs. | |
| Uniform Information Density-Based Preference Optimization This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs. Preprint | Small and efficient models via arxiv · 15 Oct, 06:18 | Not relevant Focuses on preference alignment, not small models or efficiency. |
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