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Wavenumber-Aware Deep Learning for Robust Analysis of Raman Spectra

cs.LG physics.chem-ph physics.optics Jihwan Kim, Jaeseong Heo, Yujin Kim et al. · Oct 22, 2026

Standard deep learning for spectroscopy treats spectra as fixed-grid intensity vectors, which makes models brittle when sampling grids, spectral ranges, or instruments change. This paper proposes to supply each spectral point as a pair of intensity and physical wavenumber, computing Transformer positional encodings directly from the wavenumber coordinate rather than the array index. Evaluated on in-house bacterial Raman spectra and a public 30-isolate benchmark, the approach improves robustness under partial spectral observation and on sampling grids never seen during training.

Machine-learning models for spectroscopy usually treat a spectrum as a fixed-grid intensity vector, leaving the physical coordinate implicit in the array index. Such models are tied to a single sampling grid, spectral range and resolution, which limits their use when measurement conditions change. Here we represent each spectral point as a pair of intensity and wavenumber and compute Transformer positional encodings from the wavenumber itself. On Raman spectra of three bacterial species and their synthetic mixtures, this representation remained robust to noise, sparser sampling and partial spectral coverage across convolutional, residual and Transformer networks. In controlled comparisons, wavenumber-based encoding outperformed index-based and learned encodings under partial observation and retained its performance on sampling grids never seen during training, where index-based encoding degraded. These findings were reproduced on a public 30-isolate bacterial benchmark. Explicit physical coordinates offer a simple route to robust spectral analysis under variable measurement conditions.

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ADAPT for Science: A Multi-Agent System for Self-Evolving Scientific Optimization

cs.AI cs.MA physics.optics Zhaoran Feng, Yuchen Ni, Jianan Liu et al. · Oct 22, 2026

Scientific optimization is expensive and constrained, yet the search policy is usually fixed ahead of time. ADAPT treats the complete policy $\pi=(W,O)$—where to start and how to move—as the object of optimization, using historical designs for target-aware initialization and multi-agent LLM evolution to adapt the procedure itself. Evaluated on eight held-out gravitational-wave detector topologies under matched budgets, it reduces mean loss by 54.9% versus the best classical optimizer and by 62.2% versus the best agentic baseline. The work is compelling within its domain but leaves open questions about generality and reproduction.

Scientific discovery often requires identifying a small number of high-performing designs in vast, constrained search spaces. This is difficult when evaluations are costly, feasible solutions are sparse, parameters are strongly coupled, and physical constraints are strict. Recent AI for AI methods can automatically improve algorithms and optimization procedures. However, general purpose approaches are not designed around scientific priors, physical constraints, and limited evaluation budgets. We introduce ADAPT for Science, a multi-agent framework that treats scientific optimization as two coupled decisions: where to search and how to search. ADAPT uses historical designs to identify promising and diverse initial conditions, progressively concentrates computation on favorable search directions, and evolves the optimization procedure from execution feedback. We evaluate ADAPT on the design of gravitational wave detectors using a differentiable physics simulator and eight unseen detector topologies. Under matched scientific evaluation budgets, ADAPT achieves a mean loss of 0.204 +/- 0.101, reducing loss by 54.9% relative to the best classical optimizer and by 62.2% relative to the best general purpose agentic evolution baseline. ADAPT achieves the lowest loss on all unseen topologies among the evaluated baselines. ADAPT provides a step toward scientific optimization systems that continuously learn from prior experience and their own search outcomes.

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PRISM: Breaking the O(n) Memory Wall in Long-Context LLM Inference via O(1) Photonic Block Selection

physics.optics cs.AI cs.AR Hyoseok Park, Yeonsang Park · Mar 23, 2026

Long-context LLM inference hits a memory wall: each decode step requires scanning the entire KV cache, incurring $O(n)$ memory bandwidth that cannot be solved by faster arithmetic. PRISM proposes a thin-film lithium niobate photonic accelerator that performs the block-selection similarity search in $O(1)$ optical latency using a broadcast-and-weight architecture, eliminating the $O(n)$ scan entirely. The work claims $16\times$–$32\times$ traffic reduction at 64K–128K tokens and a four-order-of-magnitude energy advantage over GPU baselines by matching photonic hardware capabilities—passive query broadcast, quasi-static microring weights, and low-precision rank output—to the selection task.

Long-context LLM inference is bottlenecked not by compute but by the O(n) memory bandwidth cost of scanning the KV cache at every decode step -- a wall that no amount of arithmetic scaling can break. Recent photonic accelerators have demonstrated impressive throughput for dense attention computation; however, these approaches inherit the same O(n) memory scaling as electronic attention when applied to long contexts. We observe that the real leverage point is the coarse block-selection step: a memory-bound similarity search that determines which KV blocks to fetch. We identify, for the first time, that this task is structurally matched to the photonic broadcast-and-weight paradigm -- the query fans out to all candidates via passive splitting, signatures are quasi-static (matching electro-optic MRR programming), and only rank order matters (relaxing precision to 4-6 bits). Crucially, the photonic advantage grows with context length: as N increases, the electronic scan cost rises linearly while the photonic evaluation remains O(1). We instantiate this insight in PRISM (Photonic Ranking via Inner-product Similarity with Microring weights), a thin-film lithium niobate (TFLN) similarity engine. Hardware-impaired needle-in-a-haystack evaluation on Qwen2.5-7B confirms 100% accuracy from 4K through 64K tokens at k=32, with 16x traffic reduction at 64K context. PRISM achieves a four-order-of-magnitude energy advantage over GPU baselines at practical context lengths (n >= 4K).

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A Framework for Closed-Loop Robotic Assembly, Alignment and Self-Recovery of Precision Optical Systems

cs.RO cs.AI physics.optics Seou Choi, Sachin Vaidya, Caio Silva et al. · Mar 23, 2026

Precision free-space optics demands sub-millimeter and sub-degree tolerances where traditional robotic pick-and-place fails. This work introduces a closed-loop robotics framework integrating hierarchical computer vision, Newton-based spatial optimization, and Bayesian angular optimization to autonomously construct, align, and maintain optical systems. The authors demonstrate this by building a tabletop laser cavity from randomly distributed components—achieving beam alignment, mode selection, and self-recovery without human intervention. The system bridges the gap between coarse robotic manipulation and the extreme precision required for functional optical experiments.

Robotic automation has transformed scientific workflows in domains such as chemistry and materials science, yet free-space optics, which is a high precision domain, remains largely manual. Optical systems impose strict spatial and angular tolerances, and their performance is governed by tightly coupled physical parameters, making generalizable automation particularly challenging. In this work, we present a robotics framework for the autonomous construction, alignment, and maintenance of precision optical systems. Our approach integrates hierarchical computer vision systems, optimization routines, and custom-built tools to achieve this functionality. As a representative demonstration, we perform the fully autonomous construction of a tabletop laser cavity from randomly distributed components. The system performs several tasks such as laser beam centering, spatial alignment of multiple beams, resonator alignment, laser mode selection, and self-recovery from induced misalignment and disturbances. By achieving closed-loop autonomy for highly sensitive optical systems, this work establishes a foundation for autonomous optical experiments for applications across technical domains.

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