国际商业机器公司

Paper waste (PW) is a major biodegradable component of non-recycled municipal solid waste (NMSW), yet it is predominantly landfilled despite its potential for material and energy recovery. This study introduces a composition-informed classification framework that categorizes PW into four distinct groups: high-cellulose (HC), high-lignin (HL), high-ash (HA), and highly contaminated (HCon) papers. This framework links material properties with suitable valorization pathways. Ten PW samples were collected across seasons to capture feedstock variability and subjected to comprehensive physicochemical and thermal characterization, including moisture content, bulk density, carbohydrate composition, proximate and ultimate analyses, thermal degradation behavior, and higher heating value (HHV). Results show that HC was the most abundant fraction, representing 37 % of PW, with up to 61 % glucan and approximately 7 % acid-insoluble residue, indicating potential suitability for enzymatic hydrolysis. HL papers exhibited relatively low moisture content (approximately 11.4 %), high carbon content (approximately 43 %), and the highest dry-basis HHV (approximately 17.1 MJ kg−1), indicating favorable fuel characteristics for thermochemical conversion. HA samples contained elevated ash associated with pigments and fillers, whereas HCon papers showed the highest moisture content, approximately 35 %, supporting composting or hydrothermal carbonization pathways. Homogenization increased bulk density threefold and reduced particle-size variability, improving handling and downstream process compatibility. Overall, 41 % of PW was technically recyclable and 56 % was compatible with composting, while hydrothermal carbonization was identified as an additional pathway for high-moisture fractions, underscoring the untapped potential of landfill-bound paper waste for circular material and energy systems.

We present ThinkPrune, a simple yet effective method for pruning the thinking length for long-thinking LLMs, which has been found to often produce inefficient and redundant thinking processes. Existing preliminary explorations of reducing thinking length primarily focus on forcing the thinking process to early exit, rather than adapting the LLM to optimize and consolidate the thinking process, and therefore the length-performance tradeoff observed so far is sub-optimal. To fill this gap, ThinkPrune offers a simple solution that continuously trains the long-thinking LLMs via reinforcement learning (RL) with an added token limit, beyond which any unfinished thoughts and answers will be discarded, resulting in a zero reward. To further preserve model performance, we introduce an iterative length pruning approach, where multiple rounds of RL are conducted, each with an increasingly more stringent token limit. We observed that ThinkPrune results in a remarkable performance-length tradeoff on the AIME24 dataset, the reasoning length of DeepSeek-R1-Distill-Qwen-1.5B can be reduced by half with only 2% drop in performance. We also observed that after pruning, the LLMs can bypass unnecessary steps while keeping the core reasoning process complete.

The generation of highly accurate models of behaviours of individual cells and cell populations through integration of high-resolution assays with advanced computational tools would transform precision medicine. Recent breakthroughs in single-cell and spatial transcriptomics and multi-omics technologies, coupled with artificial intelligence, are driving rapid progress in model development. Complementing the advances in artificial intelligence, quantum computing is maturing as a novel compute paradigm that may offer potential solutions to overcome the computational bottlenecks inherent to capturing cellular dynamics. In this Roadmap article, we discuss the advancements and challenges in spatiotemporal single-cell analysis, explore the possibility of quantum computing to address the challenges and present a case study on how quantum computing may be integrated into cell-based therapeutics. The specific confluence of quantum and classical computing with high-resolution assays may offer a crucial path towards the generation of transformative models of cellular behaviours and perturbation responses. Classical computing has inherent limitations in capturing cellular dynamics. This Roadmap article discusses how recent advancements in quantum computing could overcome bottlenecks in spatiotemporal single-cell omics analyses and how it may be integrated into cell-based therapeutics.

Large Language Model (LLM) agents are rapidly emerging as powerful systems for automating tasks across domains. Yet progress in the open-source community is constrained by the lack of high quality permissively licensed tool-agentic training data. Existing datasets are limited in diversity, realism, and complexity, particularly regarding multi-tool and multi-turn interactions. To address this gap, we introduce Toucan, which contains 1.5 million trajectories synthesized from nearly 500 real-world Model Context Protocols (MCPs). Unlike prior work, Toucan leverages authentic MCP environments to generate diverse, realistic, and challenging tasks with trajectories involving real tool execution. Our pipeline first produces a broad spectrum of tool-use queries using five distinct models, applies model-based quality filtering, and then generates agentic trajectories with three teacher models using two agentic frameworks. Rigorous rule-based and model-based validation ensures high-quality outputs. We also introduce three extensions to further diversify tasks and simulate multi-turn conversations. Models fine-tuned on Toucan outperform larger closed-source counterparts on the BFCL V3 benchmark and establish a new Pareto optimum on MCP-Universe Bench.

Interorganizational ecosystems require governance arrangements that can align diverse and often competing organizations around a shared value proposition. Although blockchain, as a form of digital governance, promises to facilitate large-scale collaboration by codifying and enforcing rules, decentralizing control, and ensuring verifiable data exchange, many blockchain-based interorganizational ecosystems nevertheless fail. Leveraging 155 interviews and detailed internal records from a large technology provider covering 81 interorganizational blockchain projects across 25 industries, supplemented by archival evidence on the trajectories of 196 projects and 70 podcast interviews, we develop a grounded theory explaining how governance misalignments trigger collaboration breakdowns. Specifically, we identify three underlying governance tradeoffs that expose tensions between blockchain's network-centric rules and actor-centric needs: consistency versus flexibility in coordination, system reliance versus actor reliance in trust and control, and ecosystem utility versus member utility in incentives. These tradeoffs are amplified or attenuated by corresponding boundary conditions related to scale and cohesion (for coordination), co-opetition (for trust and control), and value logics (for incentives). Our study advances governance theory by explaining how blockchain interacts with traditional governance, shapes critical tradeoffs, and influences ecosystem success and failure. We also offer design principles to help managers navigate the inherent governance tradeoffs in ecosystem collaboration.