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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…
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…
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…
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…
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…
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…
Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
Researchers have proposed a new planning algorithm called Graph Sparse Sampling (GSS). This algorithm is designed to help autonomous systems plan under uncertainty in continuous domains, which can be computationally…
Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
Researchers developed a machine learning model that can classify transient candidates in time-domain surveys, known as Real-Bogus classification, without needing human-labeled data. The model is trained using simulated…
LLM-as-a-Verifier: A General-Purpose Verification Framework
In this research, a general-purpose verification framework called LLM-as-a-Verifier is introduced. This framework provides detailed feedback for agentic tasks without requiring additional training. The framework…
LLM-Based Test Oracles: Source-of-Authority Taxonomy -- A Systematic Literature Review
A study on Large Language Models (LLMs) used to determine correct behavior in tests, identifies a gap in understanding the source of these models' authority. The study conducts a systematic literature review and finds…
Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
In this research, the authors propose Localized LoRA-MoE, a framework that combines localized spatial blocking with dynamic, context-conditioned routing for Large Language Models (LLMs) and high-dimensional perception…
MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution
MetaSkill-Evolve is a two-timescale framework that allows self-improving language models to improve not only their task skills but also the improvement procedure itself. The authors claim that this recursive approach…
Multiplayer Interactive World Models with Representation Autoencoders
Researchers have developed a new multiplayer world model for complex games like Rocket League, which can generate four-player matches in real time using a latent diffusion model trained on 10,000 hours of gameplay. The…
Open Problems in AI Incident Governance
The paper discusses issues with current AI incident governance systems, specifically highlighting inconsistencies in definitions, classification, monitoring, and reporting across various frameworks.
OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
The researchers have developed a multi-agent framework called OptiAgent that can take a natural language description of an Operations Research problem and produce a mathematically formulated solution along with…
PDEFlow: Autonomous Agentic PDE Pipelines for Neural Operator Learning and Solver-Free Inference
The paper introduces PDEFlow, a framework that converts user-provided ODE and PDE descriptions into neural operator pipelines. It includes a stateful input graph to validate problem specifications, a data-generation…
REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
Researchers propose a solution, called REDDIT, to correct timestamp drift issues in automatic speech recognition (ASR) systems that generate timestamps as part of the transcription. The proposed method uses…
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