Agentic AI

Reinforcement learning agents often fail to utilize small language models (SLMs) for assistance because vanilla uncertainty-gated approaches result in near-zero overwrite rates. This failure stems from insufficient egocentric context rather than a lack of model capacity. The proposed ASK+ method resolves this by providing SLMs with trajectory-aware context, such as partially revealed maps and visited positions, enabling meaningful reasoning and action guidance.

• Vanilla SLM guidance in RL fails due to context poverty, not model capacity.
• Egocentric prompts alone are insufficient for SLMs to reason effectively.
• ASK+ injects trajectory-aware context like maps and visit history.
• This approach significantly increases the SLM's contribution to agent actions.

Long-running single agents often converge prematurely on one approach, missing superior alternatives due to accumulated context and a single program state. SwarmResearch addresses this by using a Shepherd Agent to guide a population of Search Agents, each maintaining local context within separate git branches. This harness enables parallel exploration of diverse optimization strategies for open-ended problems.

• Single-agent systems risk premature convergence on suboptimal solutions due to context bloat.
• SwarmResearch uses a Shepherd Agent to steer multiple Search Agents with isolated git branches.
• Local context per agent allows parallel exploration of distinct optimization strategies.
• The approach discovers better or comparable solutions on open-ended optimization tasks.

iFLYTEK-Embodied-Omni is a unified multimodal foundation model that jointly processes vision, language, and action within a single framework. It addresses the interface bottlenecks and error compounding found in traditional cascaded pipelines that separate world modeling from action generation. The model shares multimodal representations across its visual-language, video-generation, and action-generation components to enable more precise control over extended horizons.

• Eliminates cascaded pipeline bottlenecks by unifying vision, language, and action prediction
• Reduces compound prediction errors through shared multimodal representations
• Enables precise control actions over extended horizons for general-purpose embodied agents
• Replaces specialized modules with a single Omni framework for end-to-end reasoning

This paper proposes Object-Centric Environment Modeling (OCM) to solve the scalability issues of free-form textual memory in LLM agents. The approach structures experience into two linked Python codebases: object knowledge defining entities, and procedure knowledge recording interaction patterns. This allows for executable, reusable world models that improve through online, episodic learning.

• Replaces unstructured text memory with structured, executable Python classes for better validation.
• Separates entity definitions from interaction patterns to enhance modularity and reuse.
• Supports online learning by updating models after each interaction episode.
• Addresses limitations of symbolic approaches that assume simplified or local dynamics.

Researchers introduce VERITAS, a domain-agnostic framework designed to automate the replication of scientific studies using CLI coding agents. Unlike existing benchmark-specific tools, VERITAS operates as a general-purpose utility that extracts claims from papers or repositories and executes the methodology to verify results. The system dynamically resolves execution issues during the run and judges each claim against the original evidence.

• VERITAS generalizes replication beyond narrow benchmarks by using CLI coding agents.
• It handles both paper text and code repositories as input sources.
• The framework autonomously resolves execution errors during methodology runs.
• Automates claim verification to address the bottleneck of manual replication.