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AxDafny: Agentic Verified Code Generation in Dafny
The paper presents AxDafny, a framework for generating executable code and verification artifacts in Dafny using a verifier-guided repair approach. The framework significantly improves verification success over the…
Better Understanding, Understanding Better
The paper introduces a comparative epistemic logic of understanding with level-indexed understanding modalities and a comparative connective for comparing the understanding of different agents. The framework is…
Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning
The paper introduces Medical Reasoning-aware Policy Optimization (MRPO), a reinforcement learning algorithm for medical multimodal reasoning, which aims to optimize the reasoning process and reduce cascading errors…
Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling
Researchers propose a method called CRAFT to improve traffic simulation in closed-loop environments. The proposed method aims to mitigate a local-to-global context mismatch that occurs when autoregressive traffic…
Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian
Researchers investigated cross-lingual relation extraction for Romanian using large language models (LLMs) and an English to Romanian translation pipeline. They evaluated a 31B LLM under different configurations…
Design and Implementation of Agentic Orchestrations and Orchestration of Agents
The paper discusses a framework for managing AI agents in a way that balances their autonomy with traceability and tractability. It presents various methods for orchestrating these agents, along with qualitative…
ECHO: Prune to act, trace to learn with selective turn memory in agentic RL
The paper proposes ECHO, a selective turn-memory framework for long-horizon language agents. ECHO aims to address the issues of history collapse and traceable learning by compressing completed environment turns into…
FARS: A Fully Automated Research System Deployed at Scale
The paper introduces FARS, a fully automated AI research system that can generate, execute, and write complete research papers on various AI/ML topics. In its first public deployment, FARS produced 166 papers while…
FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
The paper introduces FedXDS, a method that uses explainable AI (XAI) to help mitigate performance degradation in federated learning caused by statistical heterogeneity between client data distributions. The approach…
FinPersona-Bench: A Benchmark for Longitudinal Psychometric Stability of Autonomous Financial Agents
Researchers introduce FinPersona-Bench, a simulation benchmark to measure Mandate Salience Decay (MSD) in autonomous financial agents. MSD refers to the gradual loss of behavioral influence of initial mandates over…
FLARE-AI: Flaw Reporting for AI
Flare-AI is an open-source system designed for reporting issues in AI systems. It aims to streamline the process of flaw reporting by collecting relevant information through conditional logic and early classification…
FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
Researchers present FLORA, a deep learning framework that uses LiDAR data and auxiliary variables to predict six forest attributes in mainland France. The model outperforms season-specific models and achieves an rRMSE…
Freeform Preference Learning for Robotic Manipulation
Researchers introduce Freeform Preference Learning (FPL), a method for training robotic policies based on natural-language preferences provided by humans, rather than binary labels. The method uses the preferences to…
GR2 Technical Report
Researchers present GR2 (Generative Reasoning Re-Ranker), a framework designed to improve the performance of recommendation systems in an industrial setting. The system combines mid-training on semantic IDs…
Harnessing Textual Refusal Directions for Multimodal Safety
Researchers propose a new approach called Modality-Agnostic Refusal Steering (MARS) to improve safety in Multimodal Large Language Models without requiring unsafe multimodal data. MARS uses textual refusal directions…
Introspective Coupling: Self-Explanation Training Tracks Behavioral Change Despite Fixed Supervision
The study examines how language models (LMs) generate self-explanations for their predictions, and finds that when training LMs to explain which aspects of their inputs influenced their behavior, they often produce…
JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering
Researchers introduce JL1-CC&QA, a new benchmark for remote sensing that extends the JL1-CD dataset with two annotation layers: change captioning (CC) and change question answering (QA). The benchmark includes 5,000…
MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments
The paper introduces MECoBench, a new system for testing the collaboration abilities of multimodal large language models in visually grounded environments. The authors conduct extensive experiments with various MLLMs…
MVP-Nav: Multi-layer Value Map Planner Navigator
The paper presents a new navigation framework called MVP-Nav which addresses the challenge of zero-shot object goal navigation using only RGB perception. The framework aligns perception, planning, and control with the…
PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines
The researchers have developed a framework called PolicyGuard to ensure documents comply with an organization's specific policies or guidelines. Instead of relying on large language models for end-to-end prompting…
QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
Researchers introduce QVal, a tool that evaluates dense supervision signals for long-horizon Learning with Long Memory (LLM) agents without the need for training. This allows for a fair comparison between different…
RAISE: LLM-based Automated Heuristic Design with Robust Adversary Instance Search
The paper proposes a framework called Robust Adversary Instance Search (RAISE), which uses Large Language Models (LLMs) to design heuristics that perform well under real-world distributional shifts. The framework…
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Researchers have developed two new mechanisms - Reinforcement Learning with Metacognitive Feedback (RLMF) and Metacognitive Data Selection - to improve the performance of Language Models (LLMs). The goal is to make…
Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
This research paper discusses the need for better understanding and transparency in Artificial Intelligence (AI) used in healthcare, focusing on the explanatory process of Machine Learning models. It highlights that…
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