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Related Articles from SNS
40% boost in extreme ultraviolet conversion efficiency via simultaneous dual-beam 2-{\mu}m laser irradiation
Announce Type: new Abstract: Scaling extreme ultraviolet (EUV) source power for next-generation lithography demands higher conversion efficiency (CE) at reduced per-pulse energies. We demonstrated a 40% CE enhancement by simultaneous dual-beam irradiation of a planar Sn target with a 2090-nm, 20-ns Ho:YAG laser. Single-beam irradiation at 40 mJ yielded an EUV CE of 2.6%; splitting the same total energy equally into two beams of 20 mJ each - at identical peak intensity - raised the EUV CE to...
Reconsidering Positional Supervision in Masked Diffusion Language Model Training
Announce Type: replace Abstract: Masked diffusion language models (MDLMs) generate text by unmasking tokens in parallel and have recently emerged as alternatives to autoregressive language models. They can be viewed as parallel decoders trained with a position-wise cross-entropy (CE) loss, the same setup as non-autoregressive translation (NAT). In NAT, CE-trained parallel decoders have been argued to be sensitive to small positional shifts, since CE penalizes them harshly.
Static Electric Fields as a Model for Hydrogen-Bond-Induced Dissociation of HF and HCl
arXiv:2606.09011v1 Announce Type: new Abstract: The influence of static electric fields on the electronic structure and dissociation behavior of the polar diatomics \ce{HF} and \ce{HCl} is investigated using quantum chemical calculations. Ground- and excited-state potential energy surfaces (PESs) are computed as a function of bond distance and external electric field strength to examine field-induced modifications of chemical bonding. The calculations reveal pronounced bond softening and...
Goal2Pixel: Grounding Goals to Pixels for Vision-Language Navigation
arXiv:2606.01621v1 Announce Type: new Abstract: Vision-language models (VLMs) have become a common foundation for vision-and-language navigation in continuous environments (VLN-CE). Yet most VLM-based methods cast navigation as low-level action prediction, an interface that is ambiguous, tied to short-horizon motion primitives, and inefficient due to repeated VLM querying. We propose Goal2Pixel, a pure pixel-based paradigm that reformulates VLN-CE as navigable pixel grounding.
Beyond Waypoints: A Trajectory-Centric Waypointing Paradigm for Vision-Language Navigation
Announce Type: new Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions while navigating in real-world-like environments. Most VLN-CE approach\-es adopt a three-stage framework: a waypoint predictor proposes navigable waypoints, and a navigator selects the best waypoint, with a low-level controller executing the movement to it. However, this decoupled paradigm often leads to unreachable waypoints or inconsistencies...
Shared Doubt: Zero-shot Cross-Lingual Confidence Estimation for Language Models
arXiv:2605.31220v1 Announce Type: new Abstract: Confidence estimation (CE), i.e. quantifying the reliability of a model's prediction, has attracted great interest in the context of large language models (LLMs). However, most studies focus on English, ignoring the multilingual reality of LLM usage, while many CE methods degrade or require retraining across languages. To address this gap, we investigate whether multilingual LLMs encode shared, language-transferable confidence features.
Optimization of EUV output by experimentally validated radiation-hydrodynamic simulations across a broad laser parameter space
arXiv:2606.05948v1 Announce Type: new Abstract: Practical requirements such as improving wall-plug efficiency and reducing system footprint have become increasingly important with the introduction of extreme ultraviolet (EUV) lithography into high-volume semiconductor manufacturing. These demands motivate the development of solid-state mid-infrared lasers as alternatives to current CO2 lasers. Systematic exploration of laser-to-EUV conversion efficiency (EUV-CE) over a broad parameter space...
Fine-Tuning Improves Information Conveyance in Language Models
arXiv:2605.30844v1 Announce Type: new Abstract: Fine-tuning is often believed to reduce uncertainty and diversity in large language models, but existing analyses overlook output length, a key confounder, and therefore fail to capture how uncertainty is distributed across an entire generation rollout. To address this, we propose Canopy Entropy ($\mathrm{CE}^\star$), a measure that views language generation from a tree perspective, where ``canopy'' represents the space of all possible...
Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigation
Announce Type: new Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) poses a formidable challenge for autonomous agents, requiring seamless integration of natural language instructions and visual observations to navigate complex 3D indoor spaces. Existing approaches often falter in long-horizon tasks due to limited scene understanding, inefficient planning, and lack of robust decision-making frameworks. We introduce the \textbf{Hierarchical Semantic-Augmented...
Autoregressive Visual Generation Needs a Prologue
arXiv:2605.06137v2 Announce Type: replace Abstract: In this work, we propose Prologue, an approach to bridging the reconstruction-generation gap in autoregressive (AR) image generation. Instead of modifying visual tokens to satisfy both reconstruction and generation, Prologue generates a small set of prologue tokens prepended to the visual token sequence. These prologue tokens are trained exclusively with the AR cross-entropy (CE) loss, while visual tokens remain dedicated to reconstruction.