Human-AI scientific collaboration has evolved from tool-user relationships into co-evolutionary partnerships. When AlphaFold improved protein structure prediction, researchers engaged with an epistemic partner that transformed their approach to structure-function problems. Yet existing frameworks position AI as either sophisticated tool or potential risk, overlooking how scientific understanding emerges through recursive interaction. We introduce Cognitio Emergens (CE), a framework that captures the co-evolutionary nature of human-AI epistemic partnerships. Drawing from autopoiesis theory, social systems theory, and organizational modularity, CE integrates three components: Agency Configurations modeling how authority distributes through Directed, Contributory, and Partnership modes, with partnerships oscillating dynamically rather than following linear progression; Epistemic Dimensions capturing six capabilities along Discovery, Integration, and Projection axes, creating distinctive "capability signatures" that guide strategic development; and Partnership Dynamics identifying evolutionary forces including epistemic alienation, where researchers lose interpretive control over knowledge they formally endorse. The framework equips researchers to diagnose dimensional imbalances, institutional leaders to design governance structures supporting multiple agency configurations, and policymakers to develop evaluations beyond simple performance metrics. By reconceptualizing human-AI collaboration as fundamentally co-evolutionary, CE provides conceptual tools for cultivating partnerships that preserve epistemic integrity while enabling transformative breakthroughs neither humans nor AI could achieve independently.

In the field of medicine and healthcare, the utilization of medical expertise, based on medical knowledge combined with patients' health information is a life-critical challenge for patients and health professionals. The within-laying complexity and variety form the need for a united approach to gather, analyze, and utilize existing knowledge of medical treatments, and medical operations to provide the ability to present knowledge for the means of accurate patient-driven decision-making. One way to achieve this is the fusion of multiple knowledge sources in healthcare. It provides health professionals the opportunity to select from multiple contextual aligned knowledge sources which enables the support for critical decisions. This paper presents multiple conceptual models for knowledge fusion in the field of medicine, based on a knowledge graph structure. It will evaluate, how knowledge fusion can be enabled and presents how to integrate various knowledge sources into the knowledge graph for rescue operations.

Current knowledge-enhanced large language models (LLMs) rely on static, pre-constructed knowledge bases that suffer from coverage gaps and temporal obsolescence, limiting their effectiveness in dynamic information environments. We present Agentic-KGR, a novel framework enabling co-evolution between LLMs and knowledge graphs (KGs) through multi-round reinforcement learning (RL). Our approach introduces three key innovations: (1) a dynamic schema expansion mechanism that systematically extends graph ontologies beyond pre-defined boundaries during training; (2) a retrieval-augmented memory system enabling synergistic co-evolution between model parameters and knowledge structures through continuous optimization; (3) a learnable multi-scale prompt compression approach that preserves critical information while reducing computational complexity through adaptive sequence optimization. Experimental results demonstrate substantial improvements over supervised baselines and single-round RL approaches in knowledge extraction tasks. When integrated with GraphRAG, our method achieves superior performance in downstream QA tasks, with significant gains in both accuracy and knowledge coverage compared to existing methods.

Recent advances in long chain-of-thought (CoT) reasoning have largely prioritized answer accuracy and token efficiency, while overlooking aspects critical to trustworthiness. We argue that usable reasoning systems must be trustworthy, characterized by three properties: interpretability, faithfulness, and reliability. To this end, we propose ReFIne, a new training framework that integrates supervised fine-tuning with GRPO to encourage models to: (i) improve interpretability by producing structured, tag-based traces with high-level planning that are easier for humans to follow; (ii) enhance faithfulness by explicitly disclosing the decisive information guiding each solution, with consistent cross-section references; and (iii) promote reliability by providing self-assessments of both the derivation's soundness and the confidence of the final answer. We apply ReFIne to the Qwen3 models at multiple scales (1.7B/4B/8B) and evaluate across mathematical benchmarks of varying difficulty. Our experimental results show that ReFIne models generate clearer and better-structured reasoning traces (interpretability +44.0%), more faithfully expose their underlying decision process (faithfulness +18.8%), and offer informative confidence estimates (reliability +42.4%). These findings highlight an overlooked but important direction: reasoning models should be optimized not only for accuracy, but also for broader dimensions of trustworthiness. Large Language Models (LLMs) trained with reinforcement learning (RL) to produce extended Chain-of-Thought (CoT) traces have achieved strong performance on complex tasks such as math problem solving. These models are often referred to as Large Reasoning Models (LRMs) (Guo et al., 2025; Jaech et al., 2024). Recent progress on LRMs has largely targeted efficiency and accuracy, e.g., inference-time strategies and fine-tuning methods to shorten the reasoning length or boost accuracy (Sui et al., 2025; Muennighoff et al., 2025; Hao et al., 2024; Luo et al., 2025). However, this line of work typically treats CoT as a means to improve task performance rather than as a communication medium for users to audit and understand model behavior.

Learned world models hold significant potential for robotic manipulation, as they can serve as simulator for real-world interactions. While extensive progress has been made in 2D video-based world models, these approaches often lack geometric and spatial reasoning, which is essential for capturing the physical structure of the 3D world. To address this limitation, we introduce iMoWM, a novel interactive world model designed to generate color images, depth maps, and robot arm masks in an autoregressive manner conditioned on actions. To overcome the high computational cost associated with three-dimensional information, we propose MMTokenizer, which unifies multi-modal inputs into a compact token representation. This design enables iMoWM to leverage large-scale pretrained VideoGPT models while maintaining high efficiency and incorporating richer physical information. With its multi-modal representation, iMoWM not only improves the visual quality of future predictions but also serves as an effective simulator for model-based reinforcement learning (MBRL) and facilitates real-world imitation learning. Extensive experiments demonstrate the superiority of iMoWM across these tasks, showcasing the advantages of multi-modal world modeling for robotic manipulation. Homepage: https://xingyoujun.github.io/imowm/

Cognitive neuroscience research indicates that humans leverage cues to activate entity-centered memory traces (engrams) for complex, multi-hop recollection. Inspired by this mechanism, we introduce EcphoryRAG, an entity-centric knowledge graph RAG framework. During indexing, EcphoryRAG extracts and stores only core entities with corresponding metadata, a lightweight approach that reduces token consumption by up to 94\% compared to other structured RAG systems. For retrieval, the system first extracts cue entities from queries, then performs a scalable multi-hop associative search across the knowledge graph. Crucially, EcphoryRAG dynamically infers implicit relations between entities to populate context, enabling deep reasoning without exhaustive pre-enumeration of relationships. Extensive evaluations on the 2WikiMultiHop, HotpotQA, and MuSiQue benchmarks demonstrate that EcphoryRAG sets a new state-of-the-art, improving the average Exact Match (EM) score from 0.392 to 0.474 over strong KG-RAG methods like HippoRAG. These results validate the efficacy of the entity-cue-multi-hop retrieval paradigm for complex question answering.

We present HES-SQL, a novel hybrid training framework that advances Text-to-SQL generation through the integration of thinking-mode-fused supervised fine-tuning (SFT) with Group Relative Policy Optimization (GRPO). Our approach introduces three key innovations: (1) a skeleton-completeness scoring mechanism that enhances preference alignment between generated queries and optimal SQL structures; (2) a query-latency-aware reward system that incentivizes the generation of computationally efficient SQL queries; (3) a self-distillation process for thinking-mode completion that prevents degradation of the model's reasoning capabilities. This framework enables hybrid thinking models to switch between reasoning and non-reasoning modes while improving SQL query accuracy and execution efficiency. Experimental evaluation, conducted on MySQL 8.0 and SQLite 3.42 under controlled single-user conditions, demonstrates that HES-SQL achieves competitive performance with execution accuracies of 79.14\% and 54.9\% on the BIRD and KaggleDBQA benchmarks, respectively. Query latency is measured as the end-to-end execution time of generated queries on the DBMS, averaged over multiple runs to mitigate variance. Efficiency gains range from 11\% to 20\% relative to supervised baselines. Our results establish a new paradigm for Text-to-SQL systems that effectively balances semantic accuracy with computational efficiency through execution-informed reinforcement learning (RL). The proposed methodology has significant implications for developing robust natural language interfaces to databases and can be extended to broader structured generation tasks requiring both correctness and efficiency optimization.

Enabling embodied agents to effectively imagine future states is critical for robust and generalizable visual navigation. Current state-of-the-art approaches, however, adopt modular architectures that separate navigation planning from visual world modeling, leading to state-action misalignment and limited adaptability in novel or dynamic scenarios. To overcome this fundamental limitation, we propose UniWM, a unified, memory-augmented world model integrating egocentric visual foresight and planning within a single multimodal autoregressive backbone. Unlike modular frameworks, UniWM explicitly grounds action decisions in visually imagined outcomes, ensuring tight alignment between prediction and control. A hierarchical memory mechanism further integrates detailed short-term perceptual cues with longer-term trajectory context, enabling stable, coherent reasoning over extended horizons. Extensive experiments across four challenging benchmarks (Go Stanford, ReCon, SCAND, HuRoN) demonstrate that UniWM substantially improves navigation success rates by up to 30%, significantly reduces trajectory errors compared to strong baselines, and exhibits impressive zero-shot generalization on the unseen TartanDrive dataset. These results highlight UniWM as a principled step toward unified, imagination-driven embodied navigation.

Large Reasoning Models (LRMs) have achieved impressive performance on complex reasoning tasks by generating detailed chain-of-thought (CoT) explanations. However, these responses are often excessively long, containing redundant reasoning steps that inflate inference cost and reduce usability. Controlling the length of generated reasoning without sacrificing accuracy remains an open challenge. Through a systematic empirical analysis, we reveal a consistent positive correlation between model entropy and response length at different reasoning stages across diverse LRMs: the thinking phase exhibits higher entropy, reflecting exploratory behavior of longer responses, while the final answer phase shows lower entropy, indicating a more deterministic solution. This observation suggests that entropy at different reasoning stages can serve as a control knob for balancing conciseness and performance. Based on this insight, this paper introduces Phase Entropy Aware Reward (PEAR), a reward mechanism that incorporating phase-dependent entropy into the reward design. Instead of treating all tokens uniformly, PEAR penalize excessive entropy during the thinking phase and allowing moderate exploration at the final answer phase, which encourages models to generate concise reasoning traces that retain sufficient flexibility to solve the task correctly. This enables adaptive control of response length without relying on explicit length targets or rigid truncation rules. Extensive experiments across four benchmarks demonstrate that PEAR consistently reduces response length while sustaining competitive accuracy across model scales. In addition, PEAR demonstrates strong out-of-distribution (OOD) robustness beyond the training distribution. Our code is available at: https://github.com/iNLP-Lab/PEAR.