SeeNav-Agent: Enhancing Vision-Language Navigation with Visual Prompt and Step-Level Policy Optimization

Kardia-R1 has introduced KardiaBench, a benchmark designed to enhance emotional reasoning in conversational agents by utilizing a dataset of 178,080 QA pairs from 671 real-world profiles, addressing the limitations of existing systems that lack personalized emotional understanding.

A new adaptive curriculum mechanism called CAPO (Curriculum Advantage Policy Optimization) has been proposed to enhance cross-domain reasoning tasks in reinforcement learning. This mechanism aims to improve reasoning capabilities by utilizing advantage signals, initially focusing on positive samples to establish a solid foundation before incorporating negative signals for better discrimination.

A new framework called BountyBench has been introduced to assess the dollar impact of AI agents in cybersecurity, focusing on offensive and defensive capabilities across 25 complex systems. The framework categorizes tasks into Detect, Exploit, and Patch, with a new success indicator for vulnerability detection and 40 bug bounties covering significant OWASP risks.

The introduction of Group Adaptive Policy Optimization (GAPO) addresses the challenges of skewed reward distributions in reinforcement learning for large language models (LLMs) used in code editing. GAPO employs an adaptive approach to compute advantage estimates by utilizing an outlier-free highest-density interval, enhancing the robustness of advantage calculations in real-world scenarios.

The introduction of Optimal Rollout Allocation for Test-time Policy Optimization (OptPO) presents a new framework that enhances the adaptability of large language models (LLMs) to distribution shifts by optimizing inference budgets and reducing computational redundancy. This method employs a Bayesian sequential probability ratio test to dynamically halt sampling, allowing for efficient on-policy updates without the need for ground-truth labels.

Recent advancements in video world modeling have led to the introduction of GrndCtrl, a self-supervised framework that aligns pretrained world models with geometric and perceptual rewards. This development aims to enhance the realism and utility of generative models in navigation tasks by ensuring spatial coherence and long-horizon stability.

The study introduces PARROT (Persuasion and Agreement Robustness Rating of Output Truth), a framework aimed at assessing the accuracy degradation in large language models (LLMs) under social pressures, particularly focusing on sycophancy. It employs a double-blind evaluation to compare responses to neutral and authoritatively false questions, quantifying shifts in confidence and classifying various failure modes across 22 models using 1,302 questions from multiple domains.

The introduction of Soft Adaptive Policy Optimization (SAPO) addresses challenges in reinforcement learning (RL) for large language models (LLMs), particularly in achieving stable and effective policy optimization. SAPO replaces hard clipping with a smooth, temperature-controlled gate that adapts off-policy updates while retaining valuable learning signals, enhancing both sequence coherence and token adaptability.