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2,343 notes. Notes 1,601 to 1,650, page 33 of 47.
From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
This research proposes a theoretical framework for integrating application-layer cognitive processes into a native meta-architecture for large language models (LLMs). The framework introduces three mechanisms…
From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
The paper proposes a theoretical framework to integrate higher-order cognitive architectures into a native meta-architecture for large language models (LLMs). This is achieved by introducing three mechanisms…
From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b
Researchers present a framework using question-type-specific large language models (LLMs) to improve answer robustness and evidence grounding in biomedical question answering. The framework uses different inference…
From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b
A study presents a framework for improving the accuracy and reliability of biomedical question answering by using a large language model (LLM) that adapts its inference strategies based on the type of question (yes/no…
From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b
Researchers have developed a system for answering biomedical questions using large language models (LLMs). The system is designed to improve the robustness of answers by selecting different inference procedures for…
Harnessing Code Agents for Automatic Software Verification
The authors propose a new method for automatic software verification using large language models (LLMs) in the form of 'code agents'. Instead of wireting a fixed proof strategy, they allow the code agent to choose its…
Harnessing Code Agents for Automatic Software Verification
A new method using a general language model code agent and a verification harness proves all targeted lemmas in Iris, Rust's standard libraries, and unfinished Lean 4 port of Iris, achieving full coverage without any…
Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design
Researchers have improved the quality of process models generated by large language models (LLMs) using reinforcement learning (RL). They found that designing the reward function is crucial for optimizing beyond the…
Industry Classification of GitHub Repositories Using the North American Industry Classification System (NAICS)
Researchers have developed NAICS-GH, a corpus of 6,588 GitHub repositories labeled with industry sectors according to the North American Industry Classification System (NAICS). The labels are generated using a pipeline…
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents
The paper proposes a new method called Information Gain-based Rollout Policy Optimization (IGRPO) for improving large language model (LLM) agents in long-horizon search tasks. The method addresses the issue of existing…
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents
The paper presents Information Gain-based Rollout Policy Optimization (IGRPO), a new framework for improving large language model agents in long-horizon search tasks. IGRPO aims to allocate rollout budget more…
LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability
This research investigates the performance of large language models (LLMs) in collaborative decision-making tasks where information is not fully available to all agents. The study introduces a benchmark for such tasks…
LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis
LongCrafter is a framework designed to enhance the understanding capabilities of large language models by synthesizing long-context supervised fine-tuning data. It addresses three limitations of existing methods…
Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms
The paper proposes a multi-objective optimization control system for battery management in dairy farms using differential evolution and multi-agent deep reinforcement learning. The system aims to improve profits from…
Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms
This research introduces a multi-objective optimization control system for battery management in dairy farms that utilizes differential evolution and multi-agent deep reinforcement learning. The system aims to improve…
Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms
Researchers propose a multi-objective optimization control system for battery management in dairy farms using differential evolution and multi-agent deep reinforcement learning. The system aims to improve energy…
Rethinking Indic AI from a Lens of Cultural Heritage Preservation
The paper 'Rethinking Indic AI from a Lens of Cultural Heritage Preservation' discusses how Artificial Intelligence (AI) impacts India's linguistic and cultural foundations, focusing on the challenges and opportunities…
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
The paper presents RMISC, a large-scale, high-quality, open-access corpus of real-world multivariate time series data. The researchers pretrain four advanced Time Series Foundation Models (TSFMs) on univariate…
RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
The authors propose a framework called RSF-GLLM to improve Multi-hop Question Answering over Knowledge Graphs. The framework decouples differentiable graph reasoning from answer generation, allowing the system to learn…
RuBench: A Repository-Level Agentic Coding Benchmark with Natively Authored Russian Task Specifications
The author introduces RuBench 1.0, a benchmark for evaluating coding agents that use native language tasks instead of English. The benchmark includes 25 tasks mined from five open-source repositories and is judged by…
RuBench: A Repository-Level Agentic Coding Benchmark with Natively Authored Russian Task Specifications
The paper introduces RuBench 1.0, a new benchmark for coding agents that uses tasks specified in Russian rather than English. The benchmark is designed to measure the performance of coding agents when they are given…
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
Researchers introduce a new benchmark called Spider 2.0-AIFunc to test the ability of language models to generate AI-native SQL queries on large cloud data platforms. The benchmark includes 465 verified instances…
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
Researchers have developed Spider 2.0-AIFunc, a benchmark for testing the ability of language models to generate AI-native SQL queries on large cloud data platforms. The benchmark contains 465 verified instances across…
Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval
The authors propose a method called SkillReranker to improve the ability of agent systems to complete complex tasks by accurately selecting skills from large libraries. The method involves semantic decomposition of…
Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval
The authors propose a method called SkillReranker to improve the accuracy of selecting skills for complex tasks in agent systems. This is achieved by semantically decomposing both tasks and skills, creating a…
The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology
The Large Cancer Assistant (LCA) is a proposed framework designed to improve the scalability of clinical decision support systems in oncology. It aims to address the rigidity of current multimodal deep learning models…
TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
The paper introduces TopoBrick, a framework for zero-shot forecasting of Internet-of-Things (IoT) data in buildings, which does not require training and takes into account the physical topology, spatial hierarchy, and…
TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
The paper introduces TopoBrick, a new framework for forecasting IoT data in buildings without requiring training. It uses building knowledge graphs to create a structural skeleton and employs an agentic topology…
UI2App: Benchmarking Visual Interaction Inference in Executable Web Application Generation
Researchers have introduced UI2App, a new benchmark focused on evaluating the ability of large language models to recover application behavior from screenshots alone, without any textual or behavioral guidance. The…
VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection
The paper introduces VendorBench-100, a benchmark for evaluating deepfake image detection models across three paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors. The…
VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection
A study introduces VendorBench-100, a cross-paradigm benchmark for deepfake image detection that evaluates different models using a common protocol. The benchmark includes a diverse set of edge cases and uses the…
When do prophets profit in prediction markets?
Researchers propose a betting strategy that ensures profit when a forecaster's predictions outperform market prices under certain conditions. The strategy is shown to be the only one with such a robust profitability…
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models
The authors propose a new approach to make Electronic Health Records Foundation Models (FEMRs) more transparent by identifying the most influential tokens in the model's predictions. This is done using a…
Adaptive Inference Batching using Policy Gradients
The research proposes using reinforcement learning (RL) to create adaptive batching and routing policies for inference serving systems, which can handle bursty, heterogeneous workloads more efficiently than traditional…
Adaptive Inference Batching using Policy Gradients
The paper investigates using reinforcement learning (RL) to create adaptive batching and routing policies for inference serving systems that can handle bursty, heterogeneous workloads. The research finds that in…
Agent Data Injection Attacks are Realistic Threats to AI Agents
A new type of attack called Agent Data Injection (ADI) has been introduced, which injects malicious data disguised as trusted data to make AI agents perform unintended actions. This type of attack can bypass existing…
Agent Data Injection Attacks are Realistic Threats to AI Agents
The research paper introduces a new type of attack called Agent Data Injection (ADI), which can manipulate AI agents by injecting malicious data disguised as trusted data. This attack can cause agents to execute…
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
The paper presents AgentGym2, a new evaluation framework for language models (LLMs) that are increasingly being used in real-world production environments. Most existing benchmarks evaluate these models in simplified…
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
The paper presents AgentGym2, a new evaluation framework designed to test language models in realistic, complex scenarios that mimic real-world deployments. It highlights that most existing benchmarks do not fully…
AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales
Researchers have developed a new data-driven forecasting model, called AIFS-SUBS, to extend the capabilities of existing models beyond medium range to sub-seasonal timescales. This new model shows improved skill in…
ClassicLogic: A Knowledge-Driven Benchmark of Classic Puzzle Games for Evaluating Compositional Generalization
The paper introduces ClassicLogic, a new benchmark suite designed to evaluate an agent's ability to learn and compose problem-solving strategies in four classic logic puzzles: Sudoku, KenKen, Kakuro, and Futoshiki. The…
ClassicLogic: A Knowledge-Driven Benchmark of Classic Puzzle Games for Evaluating Compositional Generalization
The paper introduces ClassicLogic, a new benchmark suite designed to evaluate an agent's ability to learn and compose problem-solving strategies in four classic logic puzzles: Sudoku, KenKen, Kakuro, and Futoshiki. The…
Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation
Researchers introduce Cortex, a framework for long-horizon manipulation tasks that addresses the limitations of current Vision-Language-Action (VLA) models by creating executable and tractable subtask plans from…
Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation
Researchers introduce Cortex, an embodied agent framework designed to handle long-horizon manipulation tasks. The framework addresses the limitations of existing Vision-Language-Action (VLA) models by providing a…
DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
DSpark is a new speculative decoding framework for Large Language Models (LLMs) that aims to improve inference speed and maintain draft quality. It does this by using a semi-autoregressive architecture, which combines…
Evaluating and Understanding Model Editing for Medical Vision Language Models
A new clinical benchmark called M3Bench has been introduced to evaluate the effectiveness of model editing in medical vision-language models (VLMs). This benchmark tests whether an edit remains reliable, precise, and…
EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer
Researchers introduce EvoAgentBench, a new benchmark for evaluating agent self-evolution in long-term LLM systems. The benchmark focuses on procedural reuse of experience, which includes searching, debugging, and…
EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer
The paper introduces EvoAgentBench, a new benchmark for evaluating agent self-evolution in long-horizon learning machine systems. The benchmark focuses on procedural knowledge transfer across four domains: web…
From Fixed to Free Cameras: Calibration-Free View-Robust Vision-Language-Action Model
Researchers have developed a new Vision-Language-Action (VLA) model called Camera-Centric VLA (CamVLA). This model aims to improve the performance of existing VLA policies that struggle with camera variations during…
GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
The paper introduces a new multi-agent coding harness called Graph-as-Policy (GaP) for improving the reliability of robots in commercial and industrial applications, specifically focusing on variational automation…
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