π ToolsΓ1 mentions
Quantum Algorithms
βmaking a compelling case for quantum algorithmsβ
paper: Quantum Scheduling for Satellite-Based Wildfire Response β 2026-06-11
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βmaking a compelling case for quantum algorithmsβ
βwhose relevance is on the rise as utility-scale problems begin to get solved with quantum computersβ
βIn this study, we evaluate the performance of two state-of-the-art open-source ASR systems, WhisperIPA and ZIPA, that generate IPA transcriptions across diverse accents and language sources.β
βIn this study, we evaluate the performance of two state-of-the-art open-source ASR systems, WhisperIPA and ZIPA, that generate IPA transcriptions across diverse accents and language sources.β
βWe then compare full-format training with FormatMix, which expands only a subset of training items into multiple equivalent formats using either random or targeted selection.β
βWe introduce ModSleuth, an agentic system that recursively reconstructs LLM dependency graphs from public artifacts with source-grounded evidence.β
βSpeculative decoding addresses this bottleneck by employing a lightweight draft model to propose multiple future tokens that are subsequently verified in parallel by a larger target model.β
βwe introduce an expanded and more challenging benchmark based on Polish medical exams, adding over 15,000 questions, two new domains, and four structural modificationsβ
βTo facilitate further research, we make our benchmark publicly available.β
βCHAIR not only offers a practical solution for detecting hallucinations but also lays the groundwork for exploring richer representations in LLMs to improve their factuality and coherence.β
βTo make low frame rates feasible, we introduce factorized FSQ and a lightweight non-autoregressive audio LM head, scaling capacity to nearly 300 bits/frame without sacrificing efficient prediction.β
βTo make low frame rates feasible, we introduce factorized FSQ and a lightweight non-autoregressive audio LM head, scaling capacity to nearly 300 bits/frame without sacrificing efficient prediction.β
βThis work presents FE-MAD (Finite Element-Based Material learning via Automatic Differentiation), an end-to-end differentiable framework that integrates a constitutive neural network model within a JAβ¦β
βThis work presents FE-MAD (Finite Element-Based Material learning via Automatic Differentiation), an end-to-end differentiable framework that integrates a constitutive neural network model within a JAβ¦β
βThis work presents FE-MAD (Finite Element-Based Material learning via Automatic Differentiation), an end-to-end differentiable framework that integrates a constitutive neural network model within a JAβ¦β
βFE-MAD is demonstrated for two architectures: a grey-box Constitutive Artificial Neural Network (CANN), a polyconvex, fully connected model with high flexibility, and a white-box CANN, an expert-systeβ¦β
βFE-MAD is demonstrated for two architectures: a grey-box Constitutive Artificial Neural Network (CANN), a polyconvex, fully connected model with high flexibility, and a white-box CANN, an expert-systeβ¦β
βFE-MAD is demonstrated for two architectures: a grey-box Constitutive Artificial Neural Network (CANN), a polyconvex, fully connected model with high flexibility, and a white-box CANN, an expert-systeβ¦β
βGTR is architecture-agnostic and achieves strong performance across games, simulated robotic control, open-world exploration, and language model post-training.β
βThe core of STCC is the Semantic Token Codec (STC). It accepts discrete tokens as input, which maintains compatibility with foundation models while employing a residual multiple layer perceptron, i.e.β¦β
βThe comparison supports $Ο$B97X-D3/aug-cc-pVTZ geometry optimisation followed by aug-cc-pVQZ target-property calculations as a practical protocol for R-matrix modelling of NO.β
βThe comparison supports $Ο$B97X-D3/aug-cc-pVTZ geometry optimisation followed by aug-cc-pVQZ target-property calculations as a practical protocol for R-matrix modelling of NO.β
βHowever, accurate estimates of these timescales from spacecraft observations heavily rely on the accurate estimation of autocorrelation functions (ACF), which have been recently shown to depend strongβ¦β
βWe introduce a new ergodicity-based methodology to unambiguously estimate the integral timescale, and a new ACF estimator with better ergodic convergence than current ones.β
βWe present World Pilot, a VLA framework that augments the policy with priors from a World-Action Model (WAM)β
βAs part of their work, Abridg will use de-identified clinical data to further train and customize NVIDIA's Nemotron suite of open models.β
βWe introduce StereoPolicy, a visuomotor policy learning framework that directly leverages synchronized stereo image pairs to improve geometric reasoning without constructing explicit 3D representationβ¦β
βAlthough our framework is broadly applicable, we demonstrate specifically in the example of reconstruction of attosecond beating by interference of two-photon transitions (RABBIT) the influence of theβ¦β
βWe therefore propose an identity-slot aligned framework, ISAP-3D, which anchors each part with semantic identity tokens and performs identity-conditioned one-to-one layout prediction, followed by layoβ¦β
βQuantum Zeno dynamics (QZD) and dynamical decoupling (DD) are useful tools that enable the effective suppression of noise in quantum systems.β
βQuantum Zeno dynamics (QZD) and dynamical decoupling (DD) are useful tools that enable the effective suppression of noise in quantum systems.β
βWe introduce a numerically robust PGM formulation based on the Moore-Penrose pseudoinverse, replacing the standard inverse square root with a threshold-regularized variant that remains well-defined acβ¦β
βWhat are the key features and benefits of New Bing Chat mode, and how does it leverage AI to enhance the user experience?β
βI think I'm most excited about Bing as the real incarnation of Clippy.β
βWe develop a hybrid classical-quantum framework that combines pseudoinverse-based spectral preprocessing with quantum circuit realizations using block-encoding and spectral-transformation techniques.β
βemploys oblivious amplitude amplification to improve circuit-level success probabilities.β
βWe present an analytical flux control method enabling derivative removal by adiabatic gate ($Ξ¦$-DRAG) for suppressing leakage in flux tunable two-qubit gates.β
βWe introduce a quantum walk-based optimisation algorithm (QWOA) for the CVRP with homogeneous or heterogeneous vehicle fleets, addressing this challenge through a continuous-time quantum walk over a pβ¦β
βUsing the Keldysh non-equilibrium Green's function approach, we investigate charge pumping through a single-stranded helical structure described by a tight-binding model that includes either short-ranβ¦β
βUsing the Keldysh non-equilibrium Green's function approach, we investigate charge pumping through a single-stranded helical structure described by a tight-binding model that includes either short-ranβ¦β
βUsing the Keldysh non-equilibrium Green's function approach, we investigate charge pumping through a single-stranded helical structure described by a tight-binding model that includes either short-ranβ¦β
βUsing the Keldysh non-equilibrium Green's function approach, we investigate charge pumping through a single-stranded helical structure described by a tight-binding model that includes either short-ranβ¦β
βat higher frequencies, Floquet side-band mixing destroys the plateaus, leading to oscillatory currents.β
βThe proposed sampler consistently improves ADM model recall at 64x64 resolution while maintaining a comparable FID, and with a 256x256 ADM model, we showed the results visually with different combinatβ¦β
βWe have applied guidance at noisy images not on predicted noise like most diffusion models. Starting from a pretrained conditional diffusion model and classifier on ImageNet, we modify the guided reveβ¦β
βRecent work on diffusion-based tail sampling mitigates this by training an additional low-density-seeking classifier with a synthetic-vs-real discriminator, at the cost of additional networks and traiβ¦β
βthe proposed methodology extracts information from both network edges and node attributes in synthetic data, outperforming algorithms focused solely on the network or attributes when neither are very β¦β
βAcross reasoning benchmarks and model scales, ReSET improves NVFP4 reasoning accuracy by up to ~2 points over the NVFP4 baseline.β
βThis work proposes a hardware-efficient Quantum Reservoir Computing (QRC) framework for energy load forecastingβ
βTo this end, we collaborate with domain experts to release the first Supramolecular Benchmark, called SupraBench, to evaluate LLMs in chemistry reasoning.β
βEquilibrium Propagation offers a compelling alternative to traditional machine learning for training energy-based networks.β
βWe present Minimalistic PIC (MiniPIC): a minimal, flexible and fast vLLM design built from two ingredients: positional-encoding-free KV cache and user-controlled cache-reuse primitives.β
βwe propose SWITCH, a switchable latent reasoning framework. The model emits <swi> to enter latent mode and </swi> to exit.β
βProject page: https://ymlinfeng.github.io/OmniDirector.github.io/β
βwe propose ReSum, a novel RLVR framework that enables LLMs to compress and organize their reasoning trajectories through self-summarization.β
βWe propose MOSAIC, a compact continual learning framework.β
βwe propose ProFact, an agentic reinforcement learning framework for end-to-end optimization of multi-stage fact verification trajectoriesβ
βThis paper proposes a multi-field hybrid retrieval-augmented generation (RAG) framework for automated maritime RCA, utilizing a comprehensive dataset of 13,329 Korea Maritime Safety Tribunal (KMST) reβ¦β
βThis work proposes EPIG, a method that enhances emotional expressiveness at the prompt level prior to image generation.β
βWe present Brick, a multimodal router that scores each model on six capability dimensions, combines this with a per-query difficulty estimate, and dispatches via a cost-penalized geometric rule.β
βwe introduce ComCADBench, the first benchmark for agents operating real industrial CAD software.β
βwe develop ComActor, a self-correcting agent trained through a progressive three-stage framework, alongside ComForge, a scalable platform for large-scale training in Windows containers.β
βwe develop ComActor, a self-correcting agent trained through a progressive three-stage framework, alongside ComForge, a scalable platform for large-scale training in Windows containers.β
βWhile CNN-HAR achieves the highest mean macro-F1, top-performing models cluster tightly, indicating contemporary architectures have converged near a predictive performance ceiling.β
βWhen accounting for deployment efficiency, compact neural models, such as TinierHAR, and classical Random Forests define the practically relevant Pareto frontier, whereas larger recurrent and hybrid mβ¦β
βWhen accounting for deployment efficiency, compact neural models, such as TinierHAR, and classical Random Forests define the practically relevant Pareto frontier, whereas larger recurrent and hybrid mβ¦β
βTo this end, we then propose MemRefine, an LLM-guided framework that, since surface similarity poorly reflects factual value, uses similarity only to propose candidate pairs and defers delete, merge, β¦β
βOur tool, Mod-Guide, improves LLM sensitivity to minority viewpoints by leveraging contextual cues derived from lived experience.β
βThis paper introduces the Ethical Robustness Testing System (ERTS), a closed-pipeline framework that: (1) encodes ethical dilemmas into a 22-dimensional Ethical Consequence Space (ECS) grounded in estβ¦β
βLet a publisher precompute a document's KV cache, and let every other agent buy the right to load it and skip prefill.β
βWe frame the resulting agent-native prefill CDN and leave lossless KV compression and a cross-party payment layer as the open problems.β
βWe argue that the key challenge is not merely to learn purer conditions, but to organize complementary yet biased global and local conditions through multi-level allocation during generation. To this β¦β
β we propose UI-UX, an MLLM based on Qwen3-VL-4B-Thinking foundation model and enhanced via reinforcement learningβ
βWe argue that a key limitation is the lack of flexibility to proactively investigate suspicious parts of a paper based on accumulated evidence, as human reviewers do. In this paper, we explore how to β¦β
βWe introduce PolyAlign, a distribution-aware alignment framework that organizes bilingual interaction data into bucket-specific human reference distributions defined by language, interaction track, reβ¦β
βWe propose SkillCAT, a training-free framework that separates this process into three stages. Contrastive Causal Extraction (CCE) samples multiple trajectories for each task and compares same-task sucβ¦β
βWe present a low-latency real-time audio game commentary system that generates spoken commentary directly from live gameplay video.β
βWe address these challenges for the CL4Health 2026 Case Report Form (CRF) filling task by proposing a fully local, domain-adapted pipeline using the MedGemma-27B model.β
βMΓVE is designed as a living benchmark under active development; results are publicly available at https://moeve.bundesdruckerei.de/β
βWe propose HyPE (Hypergraph Persona Encoder), a framework that (i) analyzes each persona-bearing text as a (Core, Expression, Sentiment, Category) quadruple, and (ii) organizes persona elements into aβ¦β
βAn HyperGCN hypergraph neural network propagates this structure into a persona summary vector and a soft-memory bank that condition the response generator.β
βWe further propose Persistent Edge Embeddings (PEE), lightweight per-category learnable priors fused into the HyperGCN message-passing step.β
βwe propose G-Long, a graph-enhanced framework that utilizes a fine-tuned small Language Model (sLM) for structured triplet extraction and associative retrieval, significantly reducing operational costβ¦β
βAnd there's this thing called moltbook. Yeah.β
βWe propose SENTINEL, a failure-driven reinforcement learning framework that turns the Solver's rollout failures into targeted training tasks.β
βAs AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such as hidden instructions and prompt injection.β
βwe present SkillChain, which closes the production feedback loop on Skill evolution, automating the lifecycle of Skills through three stages: Skill Creator for bootstrapping from task specs and trajecβ¦β
βwe propose AGAR (Attention-Guided Adaptive Rendering), a training-free, model-agnostic method that leverages a VLM\'s own middle-to-late layer attention to identify the top-K important visual patches,β¦β
βWe further examine X-MADAM-RAG, an interpretable pipeline that decomposes evidence handling into per-document candidate extraction, visible-evidence repair, deterministic candidate grouping, and conflβ¦β
βTo facilitate future research and application, we release AutoCast, a modular framework implementing both generative models and CRPS-trained ensembles, alongside AutoSim, a flexible dataset generationβ¦β
βTo facilitate future research and application, we release AutoCast, a modular framework implementing both generative models and CRPS-trained ensembles, alongside AutoSim, a flexible dataset generationβ¦β
βWe introduce the Physics-Grounded Symbolic Architecture (PGSA) and prove three resultsβ
βMicrosoft is one of the biggest names in that latter camp. My colleague Aaron Holmes covered that decision in detail in today's AI Agenda newsletter.β
βEstablished software companies like Palantir are trying to capitalize on the growing frustration over the soaring costs of using AI tools from providers like OpenAI and Anthropic.β
βOur experiments show that the autoregressive LSTM with attention produces the most musically coherent samplesβ
βWe compare their ability to model polyphonic note sequences, learn useful latent representations, and generate stylistically coherent compositions.β
βwhile vector quantization helps mitigate posterior collapse and yields more structured outputs than conventional recurrent VAEs.β
βThe adversarial approach captures local pitch patterns but remains difficult to train and generalizes less reliably to Bach's style.β
βOn-policy distillation (OPD) has recently become a prominent post-training recipe as it combines two desirable ingredients: on-policy student trajectories and dense teacher supervision, yet how this hβ¦β
βWe introduce Fine-Tuning Any-Length Discrete Diffusion for Adaptive Decoding (A2D2), a unified framework for reward-guided fine-tuning of any-length discrete diffusion models via joint optimization ofβ¦β