The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, architectural innovations, and data curation strategies; (2) post-training techniques, including supervised fine-tuning and reinforcement learning, which adapt foundational models to downstream tasks and enhance their alignment and safety; (3) utilization strategies, such as in-context learning, prompt engineering, and agentic reasoning, that optimize real-world deployment and enable effective interaction with external environments; and (4) evaluation methods, encompassing benchmarks for key ability dimensions such as core language capabilities, reasoning, and safety, which support comprehensive and reliable assessment of model performance. Additionally, we identify critical research issues, including those concerning theoretical foundations, efficient scaling, alignment, and agentic capability, and highlight the open challenges they present. By synthesizing state-of-the-art insights and emerging trends, this survey aims to provide a systematic and comprehensive framework for understanding the trajectory, current limitations, and future directions of LLM progress.
Social dominance orientation (SDO) reflects an individual's belief in and desire for hierarchical relations among social groups, including support for unequal treatment based on group status. Although widely studied at the individual level, research has rarely investigated SDO as a team-level characteristic in organizational contexts or its implications for team outcomes. This study examines team SDO as a personality composition variable and elucidates when and how it harms team innovation. Drawing on social identity theory, we argue that under high ethnic diversity, team SDO undermines collective commitment to team goals, thereby reducing team innovation. To test these propositions, we conducted a three-wave, multilevel study of 82 teams (616 employees and their supervisors) across Canadian public government departments. Consistent with our hypotheses, ethnic diversity moderated the link between team SDO and team goal commitment, as well as the indirect effect of team SDO on innovation through goal commitment. When ethnic diversity was high, team SDO reduced team goal commitment, which subsequently hindered innovation; these effects were nonsignificant when ethnic diversity was low. We discuss the theoretical and practical implications of these findings for managing innovation in teams characterized by social dominance tendencies and ethnically diverse compositions.
The socialization literature has traditionally assumed the existence of a tension between the encouragement of newcomers' assimilation (i.e., the successful integration into the social environment) to enhance task performance and the encouragement of their differentiation (i.e., the safe expression of valuable perspectives) to enhance their creativity. However, how and when newcomers find balance between assimilation and differentiation for better performance and creativity remain unclear. Drawing on optimal distinctiveness theory, we address this limitation by developing and testing a dual-pathway model of newcomer socialization, where (a) two distinct forms of trust in supervisor (i.e., affective and cognitive) foster, namely, newcomer task performance via an assimilation process (i.e., social integration) and newcomer creativity via a differentiation process (i.e., voice); and (b) newcomer rule-following shapes these indirect effects. Results from a three-wave longitudinal survey involving 171 newcomer-supervisor dyads supported our predictions. We discuss the implications of these findings for management theory and practice.
Physical exercise can enhance motor learning by inducing neurophysiological changes that facilitate this process. This preregistered scoping review aimed to map current knowledge on the effects of physical exercise on motor learning, including experimental studies and review articles in healthy or clinical populations assessing skill acquisition and/or retention. A systematic search of eight databases (e.g. MEDLINE, EMBASE, PsycINFO) identified 66 sources, including 62 experimental studies and 4 reviews. Two researchers independently screened articles and extracted data using a pretested table, with disagreements resolved by a third researcher. Most studies involved healthy populations (83%), with clinical populations underrepresented (17%). Aerobic exercise was most commonly studied, particularly lower-limb cycling (73%), while resistance exercise was rarely examined (0.6%). Exercise intensity was predominantly high, although 6% of studies reported intensity inconsistent with the prescribed method. Motor learning outcomes varied: 55% assessed both skill acquisition and retention, whereas 45% relied solely on short-term and/or delayed retention tests. The infrequent use of long-term retention tests limits understanding of lasting effects. Overall, this review highlights gaps in the literature, including underrepresentation of clinical populations, inconsistent intensity reporting, limited research on resistance exercise, and insufficient assessment of long-term retention, which may affect interpretation of exercise effects on motor learning.
Context. Characterizing the masses, radii, and compositions of small planets orbiting M dwarfs is key to understanding their formation and identifying the best targets for atmospheric follow-up with facilities such as JWST. Methods. We refined the photometry of the TOI-4342 system using TESS and LCOGT data, and characterized the host stars with NIRPS and ESPRESSO spectroscopy. High-precision ESPRESSO radial velocities (RVs) allowed us to constrain the planetary masses and investigate their potential compositions. Results. The TOI-4336 A system is composed of a sub-Neptune with a period of 16.34 days, a radius of 2.14 +/- 0.08 R-circle plus, and a mass of 3.33 +/- 0.36 M-circle plus, along with an inner super-Earth on a 7.59-day orbit with a radius of 1.25 +/- 0.07 R-circle plus and a mass of 1.55 +/- 0.13 M-circle plus. The TOI-4342 system hosts two sub-Neptunes of similar sizes (2.33 +/- 0.09 R-circle plus and 2.35 +/- 0.09 R-circle plus), with periods of 5.54 and 10.69 days. Their masses are measured to be 7.3 +/- 1.3 M-circle plus and 4.8 +/- 1.4 M-circle plus, respectively. The RVs also reveal a planet candidate around TOI-4342, most likely non-transiting, with a period of 47.5 days and a minimum mass of 17.8 +/- 3.0 M-circle plus. Conclusions. With precise radii and masses, we derived bulk densities and explored possible compositions. The TOI-4336 A subNeptune and super-Earth have densities of 1.87 +/- 0.30 and 4.35 +/- 0.79 g cm(-3), while the two similar-sized sub-Neptunes in TOI-4342 show distinct densities of 3.18 +/- 0.67 and 2.01 +/- 0.63 g cm(-3). Using an inference model, we find that TOI-4336 A b, TOI-4342 b, and TOI-4342 c have an atmosphere mass fraction (AMF) of similar to 3.7%, similar to 1.8%, and similar to 2.9%, respectively, while the super-Earth TOI-4336 A c could contain similar to 2% of water or have a core-to-mass fraction (CMF) of similar to 31%. All four planets are excellent targets for future atmospheric characterization with JWST, and their multi-planet nature makes them especially interesting for comparative planetology. Notably, TOI-4336 A b stands out as one of the best known targets in its size and temperature regime, with a transmission spectroscopy metric (TSM) of 138, comparable to benchmark planets such as K2-18 b and LHS 1140 b. Its inner sibling, TOI-4336 A c, may also be of interest for emission spectroscopy and exploring the "cosmic shoreline", similarly to the Rocky Worlds DDT JWST program.
