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Researcher profile

Shuai Peng

Shuai Peng contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.quant-gasComputer Vision

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Published work

3 published item(s)

preprint2026arXiv

Accelerating evaporative cooling of a strongly interacting Fermi gas by tilting the optical trap with a magnetic field gradient

We present a rapid evaporative cooling scheme for a strongly interacting $^{6}\mathrm{Li}$ Fermi gas in an optical dipole trap. The method uses a magnetic-field-gradient--induced tilt of the trapping potential to accelerate cooling in the unitarity-limited regime. In evaporation based only on lowering the optical trap depth, the unitarity-limited scattering cross section can support runaway cooling; however, the cooling rate slows around $T/T_F \simeq 0.5$, and the runaway behavior is no longer maintained. We improve on this approach by applying a magnetic-field gradient when the gas temperature reaches about half the Fermi temperature. The induced tilt opens an escape channel for energetic atoms while keeping the trap frequencies nearly unchanged. This modification increases the cooling speed and cools the gas below the superfluid transition temperature, reaching $T/T_F = 0.16$ on a timescale of $\sim 25\,\mathrm{ms}$. Our results provide a simple and robust route for rapidly cooling a strongly interacting Fermi gas into the superfluid regime, facilitating studies of the physics of unitary Fermi superfluids.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context

Long-context modeling is becoming a core capability of modern large vision-language models (LVLMs), enabling sustained context management across long-document understanding, video analysis, and multi-turn tool use in agentic workflows. Yet practical training recipes remain insufficiently explored, particularly for designing and balancing long-context data mixtures. In this work, we present a systematic study of long-context continued pre-training for LVLMs, extending a 7B model from 32K to 128K context with extensive ablations on long-document data. We first show that long-document VQA is substantially more effective than OCR transcription. Building on this observation, our ablations further yield three key findings: i) for sequence-length distribution, balanced data outperforms target-length-focused data (e.g., 128K), suggesting that long-context ability requires generalizable key-information retrieval across various lengths and positions; ii) retrieval remains the primary bottleneck, favoring retrieval-heavy mixtures with modest reasoning data for task diversity; and iii) pure long-document VQA largely preserves short-context capabilities, suggesting that instruction-formatted long data reduces the need for short-data mixing. Based on these findings, we introduce MMProLong, obtained by long-context continued pre-training from Qwen2.5-VL-7B with only a 5B-token budget. MMProLong improves long-document VQA scores by 7.1% and maintains strong performance at 256K and 512K contexts beyond its 128K training window, without additional training. It further generalizes to webpage-based multimodal needle retrieval, long-context vision-text compression, and long-video understanding without task-specific supervision. Overall, our study establishes a practical LongPT recipe and an empirical foundation for advancing long-context vision-language models.

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preprint2022arXiv

Controllable Production of Degenerate Fermi Gases of $^6$Li Atoms in the 2D-3D Crossover

The many-body physics in the dimensional crossover regime attracts much attention in cold atom experiments, but yet to explore systematically. One of the technical difficulties existed in the experiments is the lack of the experimental technique to quantitatively tune the atom occupation ratio of the different lattice bands. In this letter, we report such techniques in a process of transferring a 3D Fermi gas into a 1D optical lattice, where the capability of tuning the occupation of the energy band is realized by varying the trapping potentials of the optical dipole trap (ODT) and the lattice, respectively. We could tune a Fermi gas with the occupation in the lowest band from unity to 50$\%$ quantitatively. This provides a route to experimentally study the dependence of many-body interaction on the dimensionality in a Fermi gas.

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