arxiv6d ago

Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction

arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to recommend, where each SID is a short sequence of discrete hierarchical content codes. A single instruc

arxivOct 13

Beyond Semantic IDs: Finite-Beam Reachability and Continuous-Query Generation in Generative Recommendation

arXiv:2610.12977v1 Announce Type: new Abstract: Generative recommendation (GR) of social media platforms commonly retrieves items by autoregressively decoding discrete Semantic IDs (SIDs) with finite-width beam search. This work examines how this output interface constrains whether held-out targets

techcrunchOct 5

Reflection debuts Beam, an open-weight AI model to rival Chinese models at lower compute cost

Reflection is aiming Beam and future models at enterprises and sovereign nations. The pitch is to build “AI factories,” a product that would let institutions build their own customized, local AI system by training Reflection’s AI models on their own proprietary data.

arxivOct 5

Multi-Modal Environment-Aware Beam Management for Massive MIMO: A Geometry-Driven Virtual Base Station Framework

arXiv:2606.26567v1 Announce Type: cross Abstract: High-frequency massive multiple-input multiple-output (MIMO) systems promise ultra-high data rates. However, efficient beam management remains challenging due to the prohibitive beam training overhead and intricate coordination required in multi-user

arxivOct 3

Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits

arXiv:2604.14908v2 Announce Type: replace Abstract: We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multiple single-antenna user equipments (UEs) with a unique beam per UE and

arxivOct 2

Making Grid Beam Search Less Greedy

arXiv:2609.39368v1 Announce Type: new Abstract: A common formalism for constraining the output of autoregressive text generation models involves lexical constraints, words or phrases which are required to occur in the generated text. DFA-constrained beam search and grid beam search are two widely us

arxivOct 2

Provable Test-Time Scaling for Beam Search in LLM Reasoning

arXiv:2609.38672v1 Announce Type: new Abstract: Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning invalid reasoning paths early, leading to significantly improved reasoning efficiency and more favorabl

arxivOct 1

Beam Search as Test-Time Self-Distillation via Counterfactual Contexts

arXiv:2609.37041v1 Announce Type: new Abstract: Self-Distillation Fine-Tuning (SDFT) enables a language model to act as its own teacher: by conditioning on a demonstration, the model produces an implicit reward via pointwise mutual information, which guides on-policy learning without external superv

arxivSep 25

Self-Localizing MIMO Beam Mapping with Continuously Evolving Channel Memory

arXiv:2511.17007v3 Announce Type: replace-cross Abstract: Machine learning has greatly advanced data-driven channel modeling and resource optimization. However, most existing methods require accurately location-labeled datasets, which are costly to collect and maintain in dynamic environments. This

arxivSep 24

Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinement

arXiv:2609.28110v1 Announce Type: cross Abstract: Dental cone-beam computed tomography (CBCT) systems often employ detector configurations that provide a truncated field of view (FOV) that only captures a small part of the patient's anatomy. In this work, we aim to reconstruct an extended FOV using

googleSep 23

Google Beam expands with new regions, partners, and customers

<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Google_Beam_hero.max-600x600.format-webp.webp">We’re expanding Google Beam to five new countries, and partnering with Industrious for an extended network.

arxivSep 23

Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction

arXiv:2609.18368v2 Announce Type: replace-cross Abstract: Classical CSI feedback in FDD massive MIMO transmits a compressed reconstruction of the channel, optimizing fidelity to the original signal regardless of the downstream task. We propose a semantic communication perspective: instead of reconst

arxivSep 15

Clinical Reasoning Under a Partially Observed Objective in Cone Beam CT Report Generation

arXiv:2609.13238v1 Announce Type: new Abstract: Maxillofacial report generation from cone beam computed tomography is scored here by a composite objective placing 80% of its weight on a large language model judgement of factual entailment and 20% on lexical overlap, of which only the lexical fifth i

arxivSep 10

Novel hybrid protein scaffold gap filling using weighted machine learning ensemble, beam search, and mass-constrained reranking

arXiv:2609.05436v1 Announce Type: cross Abstract: Protein scaffold gap filling is an important computational task in protein sequence reconstruction, where missing amino acid regions must be inferred from incomplete scaffold information. This study proposes a hybrid machine learning and mass constra

arxivSep 2

BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Management

arXiv:2609.00615v1 Announce Type: cross Abstract: The evolution toward sixth-generation (6G) wireless networks is driving larger antenna arrays and highly directional multi-beam transmission, making accurate knowledge of beam-dependent spatial coverage important for beam management and environment-a

arxivSep 1

TAAL: Mitigating Early Beam Pruning in Generative Recommendation via Temporal Autoregressive Alignment

arXiv:2608.29179v1 Announce Type: cross Abstract: Generative recommendation encodes items as hierarchical semantic identifiers (SIDs) and retrieves the next item through autoregressive decoding. Standard next-token prediction, however, does not explicitly cover the multimodal transitions present in

arxivSep 1

Artifact Reduction in Undersampled 3D Cone-Beam CTs using a Hybrid 2D-3D CNN Framework

arXiv:2602.08727v2 Announce Type: replace-cross Abstract: Undersampled CT volumes minimize acquisition time and radiation exposure but introduce artifacts degrading image quality and diagnostic utility. Reducing these artifacts is critical for high-quality imaging. We propose a computationally effic

arxivAug 27

Beam Search, Self-Consistency, and the Limits of Inference-Time Scaling for Grammar-Constrained Text-to-SQL in Small Language Models

arXiv:2608.25761v1 Announce Type: new Abstract: One common trade-off in the use of large language models involves reducing the size of the model while increasing the amount of computation at inference time, for example by using a wider beam search. In this paper, we examine the constrained case of t

arxivAug 3

Adjudicated Captioning: Multi-Agent Alignment Scoring and Consensus-Distilled Beam Arbitration for Strict Zero-Shot Image Captioning

arXiv:2607.28986v1 Announce Type: cross Abstract: Zero-shot image captioning (ZIC) describes images without paired image-caption supervision during captioner training, relying on text-only corpora and frozen pretrained image-text scorers. Existing retrieval-augmented methods score image-text alignme

arxivAug 3

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

arXiv:2603.24917v3 Announce Type: replace Abstract: Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences. Probabilistic extraction -- computing the probability of generating a target suffix given a pr

arxivJul 29

SpectONet: A Physics-Guided Spectral Deep Operator Network for Euler-Bernoulli Beam Dynamics

arXiv:2607.25790v1 Announce Type: cross Abstract: This paper proposes a novel physics-guided spectral deep operator network, termed SpectONet, for solving Euler-Bernoulli beam (EBB) vibration problems. The proposed framework integrates the operator-learning capability of DeepONet with physics-inform

arxivJul 24

Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space

arXiv:2412.01748v2 Announce Type: replace Abstract: Complex dynamical systems, such as particle accelerators, often require intricate and time-consuming tuning procedures to achieve optimal performance. In many cases, these procedures must also estimate the optimal system parameters governing the dy

arxivJul 22

Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark

arXiv:2607.18294v1 Announce Type: new Abstract: Machine learning surrogate models are increasingly being explored in engineering product development to augment simulation-driven design, offering near-instantaneous predictions that complement computationally expensive high-fidelity analyses. However,

arxivJul 14

Data-Driven Forward and Inverse Modeling of V-Beam Thermal Sensors

arXiv:2607.09752v1 Announce Type: cross Abstract: This paper presents a machine learning framework for data-driven inverse design of V-beam thermal sensors. The goal is to determine the optimal sensor geometry: beam inclination angle, beam length and beam width that achieves a target displacement un

arxivJul 14

FlashTrie: A GPU-Accelerated Constrained Beam Search for Generative Retrieval

arXiv:2607.10044v1 Announce Type: new Abstract: Constrained decoding is essential in generative retrieval, where document identifiers generated directly from a query must exactly match a predefined library of valid IDs. At scale, decoding is often constrained using a trie with beam search but most i

arxivJul 10

RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization

arXiv:2607.08045v1 Announce Type: cross Abstract: Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks. Predicting the angular power spectrum (APS) from geometry is difficult, bec

arxivJul 2

Meta-Transfer Learning for mmWave Beam Alignment

arXiv:2607.00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging. Meta-learning and transfer learning have been explored to enable deep learning-based beam predict

arxivJun 24

WiFi-Based People Counting Using Beam-Steerable Antennas: A Test-bed Study

arXiv:2606.23710v1 Announce Type: cross Abstract: Ubiquitous perception through RF signals is a pivotal opportunity for future technology: it enables personalized services such as smart living, remote healthcare, automated logistics or interaction through free-space gestures. The ubiquity of Wi-Fi a

arxivJun 10

STORM: Stepwise Token Optimization with Reward-Guided Beam Search

arXiv:2606.10621v1 Announce Type: cross Abstract: Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specialized index, rebuilt whenever the model changes. Lexical retrievers like BM25 stay efficient and tra

arxivJun 5

Beam-Plasma Collective Oscillations in Intense Charged-Particle Beams: Dielectric Response Theory, Langmuir Wave Dispersion, and Unsupervised Detection via Prometheus

arXiv:2603.10457v4 Announce Type: replace-cross Abstract: We develop a theoretical and computational framework for beam-plasma collective oscillations in intense charged-particle beams at intermediate energies (10-100 MeV). In Part I, we formulate a kinetic field theory governed by the Vlasov-Poisso

arxivMay 28

Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction

arXiv:2605.28124v1 Announce Type: new Abstract: The goal of this work is to reduce the effect of photon noise in dental cone-beam CT reconstruction. We consider an inverse problem formulation and develop a databased prior. To this end, we simulate fan-beam acquisitions and add photon noise to the pr

arxivMay 26

BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models

arXiv:2601.22925v3 Announce Type: replace-cross Abstract: Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine-tuning (SFT) to adapt LLMs to recommendation scenarios, and utilize beam search during

googleMay 20

A new experiment brings better group meetings to Google Beam

<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Screenshot_2026-05-15_at_4.21.2.max-600x600.format-webp.webp">See and hear your colleagues in true-to-life size and sound, making hybrid meetings feel more inclusive and connected.

arxivMay 18

GeoGS-CE: Learning Delay--Beam Channel Priors with 3D Gaussians for High-Mobility Scenarios

arXiv:2605.16094v1 Announce Type: cross Abstract: Wideband channel estimation (CE) in high-mobility scenarios remains challenging because channel responses vary rapidly, while practical systems can allocate only sparse pilots to accommodate dense users. Fortunately, many high-mobility environments,

arxivMay 16

BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE

arXiv:2605.14438v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standard MoE employs a fixed Top-K routing strategy, leading to redundant computation and suboptimal infer

arxivMay 13

Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance

arXiv:2605.12453v1 Announce Type: cross Abstract: To address the issues of high interruption time and measurement report overhead under user equipment (UE) mobility especially in high speed 5G use cases the use of AI/ML techniques (AI/ML beam management and mobility procedures) have been proposed. T

arxivMay 12

V-ABS: Action-Observer Driven Beam Search for Dynamic Visual Reasoning

arXiv:2605.10172v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable success in general perception, yet complex multi-step visual reasoning remains a persistent challenge. Although recent agentic approaches incorporate tool use, they often neglect criti

arxivMay 8

Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search

arXiv:2512.09538v2 Announce Type: replace-cross Abstract: Consistency-based methods have emerged as an effective approach to uncertainty quantification (UQ) in large language models. These methods typically rely on several generations obtained via multinomial sampling, measuring their agreement leve

arxivApr 21

B-PASTE: Beam-Aware Pattern-Guided Speculative Execution for Resource-Constrained LLM Agents

arXiv:2604.16469v1 Announce Type: cross Abstract: LLM agents execute in an interleaved reasoning-and-action loop, where future tool calls cannot be launched until the current reasoning step completes. This serial dependency inflates end-to-end latency and leaves the model idle while waiting for tool

arxivApr 16

BEAM: Bi-level Memory-adaptive Algorithmic Evolution for LLM-Powered Heuristic Design

arXiv:2604.12898v1 Announce Type: new Abstract: Large Language Model-based Hyper Heuristic (LHH) has recently emerged as an efficient way for automatic heuristic design. However, most existing LHHs just perform well in optimizing a single function within a pre-defined solver. Their single-layer evol

arxivApr 14

Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks

arXiv:2604.09653v1 Announce Type: cross Abstract: Beam alignment is a key challenge in directional mmWave and THz systems, where narrow beams require accurate yet low-overhead training. Existing learning-based approaches typically predict a single beam and do not quantify uncertainty, limiting adapt

arxivApr 9

Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam

arXiv:2604.07025v1 Announce Type: cross Abstract: This article presents a novel and comprehensive approach for analyzing bending behavior of the tapered perforated beam under an exponential load. The governing differential equation includes important factors like filling ratio ($\alpha$), number of

arxivApr 7

PhaseFlow4D: Physically Constrained 4D Beam Reconstruction via Feedback-Guided Latent Diffusion

arXiv:2604.03885v2 Announce Type: cross Abstract: We address the problem of recovering a time-varying 4D distribution from a sparse sequence of 2D projections - analogous to novel-view synthesis from sparse cameras, but applied to the 4D transverse phase space density $\rho(x,p_x,y,p_y)$ of charged