TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models

Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. However, the continual learning aspect of these aligned LLMs has been largely overlooked. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs, owing to both their simplicity and the models' potential exposure during instruction tuning. In this paper, we introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs. TRACE consists of 8 distinct datasets spanning challenging tasks including domain-specific tasks, multilingual capabilities, code generation, and mathematical reasoning. All datasets are standardized into a unified format, allowing for effortless automatic evaluation of LLMs. Our experiments show that after training on TRACE, aligned LLMs exhibit significant declines in both general ability and instruction-following capabilities. For example, the accuracy of llama2-chat 13B on gsm8k dataset declined precipitously from 28.8\% to 2\% after training on our datasets. This highlights the challenge of finding a suitable tradeoff between achieving performance on specific tasks while preserving the original prowess of LLMs. Empirical findings suggest that tasks inherently equipped with reasoning paths contribute significantly to preserving certain capabilities of LLMs against potential declines. Motivated by this, we introduce the Reasoning-augmented Continual Learning (RCL) approach. RCL integrates task-specific cues with meta-rationales, effectively reducing catastrophic forgetting in LLMs while expediting convergence on novel tasks

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Breaking Low-Strain and Deep-Potassiation Trade-Off in Alloy Anodes via Bonding Modulation for High-Performance K‑Ion Batteries

Alloy anode materials have garnered unprecedented attention for potassium storage due to their high theoretical capacity. However, the substantial structural strain associated with deep potassiation results in serious electrode fragmentation and inadequate K-alloying reactions. Effectively reconciling the trade-off between low-strain and deep-potassiation in alloy anodes poses a considerable challenge due to the larger size of K-ions compared to Li/Na-ions. In this study, we propose a chemical bonding modulation strategy through single-atom modification to address the volume expansion of alloy anodes during potassiation. Using black phosphorus (BP) as a representative and generalizing to other alloy anodes, we established a robust P–S covalent bonding network via sulfur doping. This network exhibits sustained stability across discharge–charge cycles, elevating the modulus of K–P compounds by 74%, effectively withstanding the high strain induced by the potassiation process. Additionally, the bonding modulation reduces the formation energies of potassium phosphides, facilitating a deeper potassiation of the BP anode. As a result, the modified BP anode exhibits a high reversible capacity and extended operational lifespan, coupled with a high areal capacity. This work introduces a new perspective on overcoming the trade-off between low-strain and deep-potassiation in alloy anodes for the development of high-energy and stable potassium-ion batteries

Breaking Low-Strain and Deep-Potassiation Trade-Off in Alloy Anodes via Bonding Modulation for High-Performance K‑Ion Batteries

Alloy anode materials have garnered unprecedented attention for potassium storage due to their high theoretical capacity. However, the substantial structural strain associated with deep potassiation results in serious electrode fragmentation and inadequate K-alloying reactions. Effectively reconciling the trade-off between low-strain and deep-potassiation in alloy anodes poses a considerable challenge due to the larger size of K-ions compared to Li/Na-ions. In this study, we propose a chemical bonding modulation strategy through single-atom modification to address the volume expansion of alloy anodes during potassiation. Using black phosphorus (BP) as a representative and generalizing to other alloy anodes, we established a robust P–S covalent bonding network via sulfur doping. This network exhibits sustained stability across discharge–charge cycles, elevating the modulus of K–P compounds by 74%, effectively withstanding the high strain induced by the potassiation process. Additionally, the bonding modulation reduces the formation energies of potassium phosphides, facilitating a deeper potassiation of the BP anode. As a result, the modified BP anode exhibits a high reversible capacity and extended operational lifespan, coupled with a high areal capacity. This work introduces a new perspective on overcoming the trade-off between low-strain and deep-potassiation in alloy anodes for the development of high-energy and stable potassium-ion batteries

Breaking Low-Strain and Deep-Potassiation Trade-Off in Alloy Anodes via Bonding Modulation for High-Performance K‑Ion Batteries

Alloy anode materials have garnered unprecedented attention for potassium storage due to their high theoretical capacity. However, the substantial structural strain associated with deep potassiation results in serious electrode fragmentation and inadequate K-alloying reactions. Effectively reconciling the trade-off between low-strain and deep-potassiation in alloy anodes poses a considerable challenge due to the larger size of K-ions compared to Li/Na-ions. In this study, we propose a chemical bonding modulation strategy through single-atom modification to address the volume expansion of alloy anodes during potassiation. Using black phosphorus (BP) as a representative and generalizing to other alloy anodes, we established a robust P–S covalent bonding network via sulfur doping. This network exhibits sustained stability across discharge–charge cycles, elevating the modulus of K–P compounds by 74%, effectively withstanding the high strain induced by the potassiation process. Additionally, the bonding modulation reduces the formation energies of potassium phosphides, facilitating a deeper potassiation of the BP anode. As a result, the modified BP anode exhibits a high reversible capacity and extended operational lifespan, coupled with a high areal capacity. This work introduces a new perspective on overcoming the trade-off between low-strain and deep-potassiation in alloy anodes for the development of high-energy and stable potassium-ion batteries

Breaking Low-Strain and Deep-Potassiation Trade-Off in Alloy Anodes via Bonding Modulation for High-Performance K‑Ion Batteries

Alloy anode materials have garnered unprecedented attention for potassium storage due to their high theoretical capacity. However, the substantial structural strain associated with deep potassiation results in serious electrode fragmentation and inadequate K-alloying reactions. Effectively reconciling the trade-off between low-strain and deep-potassiation in alloy anodes poses a considerable challenge due to the larger size of K-ions compared to Li/Na-ions. In this study, we propose a chemical bonding modulation strategy through single-atom modification to address the volume expansion of alloy anodes during potassiation. Using black phosphorus (BP) as a representative and generalizing to other alloy anodes, we established a robust P–S covalent bonding network via sulfur doping. This network exhibits sustained stability across discharge–charge cycles, elevating the modulus of K–P compounds by 74%, effectively withstanding the high strain induced by the potassiation process. Additionally, the bonding modulation reduces the formation energies of potassium phosphides, facilitating a deeper potassiation of the BP anode. As a result, the modified BP anode exhibits a high reversible capacity and extended operational lifespan, coupled with a high areal capacity. This work introduces a new perspective on overcoming the trade-off between low-strain and deep-potassiation in alloy anodes for the development of high-energy and stable potassium-ion batteries