RNNs like xLSTM with vertically chunked inference strategy for efficient memory usage: arxiv.org/abs/2604.18199 Chunking enables a linear-time and constant-memory like for TFLA for xLSTM arxiv.org/abs/2503.14376 Chunking blocks via recurrent updates speeds up computation considerably.

A high-performance trading architecture built around xLSTM: allenarch.dev/blog/combini... The model is used as a market-state encoder, while reinforcement learning handles the trading decisions. This performance of xLSTM should be confirmed by further investigations.

xLSTM is more expressive than Transformer, Mamba: arxiv.org/abs/2603.03612 *nonlinear RNNs: sLSTM, LSTM *DLPR linear RNNs: mLSTM, RWKV-7, DeltaNet *Non PNC1-complete: Mamba, Transformer “fundamental expressivity gaps between linear and nonlinear RNNs.” World models require nonlinear RNNs.

xLSTM Distillation: arxiv.org/abs/2603.15590 Near-lossless distillation of quadratic Transformer LLMs into linear-time xLSTM architectures enables cost- and energy-efficient alternatives without sacrificing performance. Efficient xLSTM variants of instruction-tuned Llama, Qwen, and Olmo models.

Symbol-equivariant Recurrent Reasoning Models (SE-RRM) SE-RRM advances HRM and TRM -- guaranteed identical solutions for problems with permuted colors (ARC AGI) or digits (Sudoku). Coolest part: extrapolation to larger problem sizes!!! P: arxiv.org/abs/2603.02193 C: github.com/ml-jku/SE-RRM

xLSTM for Financial Time Series: arxiv.org/abs/2603.01820 "VLSTM achieved the highest overall Sharpe ratio" "VxLSTM and LPatchTST exhibited superior downside-adjusted characteristics" “xLSTM achieves the highest portfolio-level cost buffer" xLSTM excels in financial time series as TiRex does.

Drug design is significantly accelerated by ConGLUDe. Protein–small-molecule interactions can be screened orders of magnitude faster using the ConGLUDe approach.

xLSTM for Lensed Gravitational Waves: arxiv.org/abs/2512.21370 sLSTM models fine-grained temporal structures, while mLSTM finds large-scale global patterns. xLSTM achieves AUC beyond 0.99, a TPR above 98% a FPR below 1% and is robust against noise, lens type, lens mass. Cool xLSTM application.

xLSTM for Real-Time DNS Tunnel Detection: arxiv.org/abs/2512.09565 DNS-HyXNet = xLSTM for DNS tunnels. DNS-HyXNet has 99.99% accuracy, with F1-scores exceeding 99.96%, and per-sample detection latency of just 0.041 ms, confirming its scalability and real-time readiness. wow!

xLSTM for PINNs that learn PDEs: arxiv.org/abs/2511.12512 “Across four PDEs under matched size and budget, xLSTM-PINN consistently reduces MSE, RMSE, MAE, and MaxAE with markedly narrower error bands.” “cleaner boundary transitions with attenuated high-frequency ripples”

Measuring AI Progress in Drug Discovery - A NEW LEADERBOARD IN TOWN 2015-2025: turns out that there's hardly any improvement. AI bubble? GPT is at 70% for this task, whereas the best methods get close to 85%. Leaderboard: huggingface.co/spaces/ml-jk... P: arxiv.org/abs/2511.14744

xLSTM for Vehicle Trajectory Prediction: arxiv.org/abs/2511.00266 X-TRACK based on xLSTM achieves SOTA. “Compared to state-of-the-art baselines, X-TRACK achieves performance improvement by 79% at the 1-second prediction and 20% at the 5-second prediction in the case of highD” Again xLSTM excels.

xLSTM for robotic manipulation systems via diffusion-based imitation learning: arxiv.org/abs/2510.20406 PMP leverages xLSTM to denoise actions for robotics. “PMP not only achieves state-of-the-art performance but also offers significantly faster training and inference.” xLSTM excels in robotics.

Tenure Track in quantum informatics! Super cool position. Super cool team. World-class research. Scientifically outstanding work.

xLSTM for Toxic Comment Classification: arxiv.org/abs/2510.17018 “On the Jigsaw Toxic Comment benchmark, xLSTM attains 96.0% accuracy and 0.88 macro-F1, outperforming BERT by 33% on threat and 28% on identity_hate categories, with 15× fewer parameters and <50 ms inference latency.” xLSTM is fast!

gLSTM extends xLSTM to a graph neural network architecture: arxiv.org/abs/2510.08450 "gLSTM mitigates sensitivity over-squashing and capacity over-squashing." "gLSTM achieves comfortably state of the art results on the Diameter and Eccentricity Graph Property Prediction tasks"

xLSTM for Intrusion Detection: arxiv.org/abs/2510.08333 "The xLSTM-based IDS achieves an F1-score of 98.9%, surpassing the transformer-based model at 94.3%." xLSTM is faster than transformer when using fast kernels as provided in github.com/nx-ai/mlstm_... and github.com/NX-AI/flashrnn

xLSTM for long-term context using short sliding windows: arxiv.org/abs/2509.24552 "SWAX, a hybrid consisting of sliding-window attention and xLSTM." "SWAX trained with stochastic window sizes significantly outperforms regular window attention both on short and long-context problems."

xLSTM shines as an Electrocardiogram (ECG) foundation model: arxiv.org/abs/2509.10151 "xECG achieves superior performance over earlier approaches, defining a new baseline for future ECG foundation models." xLSTM is perfectly suited for time series prediction as shown by TiRex.

xLSTM excels in time series forecasting: arxiv.org/abs/2509.01187 . Introduces "stochastic xLSTM" (StoxLSTM). "StoxLSTM consistently outperforms state-of-the-art baselines with better robustness and stronger generalization ability." We know that xLSTM is king at time series from our TiRex.

xLSTM for Cellular Traffic Forecasting: arxiv.org/abs/2507.19513 "Empirical results showed a 23% MAE reduction over the original STN and a 30% improvement on unseen data, highlighting strong generalization." xLSTM shines again in time series forecasting.

xLSTM for Monaural Speech Enhancement: arxiv.org/abs/2507.04368 xLSTM has superior performance vs. Mamba and Transformers but is slower than Mamba. New Triton kernels: xLSTM is faster than MAMBA at training and inference: arxiv.org/abs/2503.13427 and arxiv.org/abs/2503.14376

xLSTM for Aspect-based Sentiment Analysis: arxiv.org/abs/2507.01213 Another success story of xLSTM. MEGA: xLSTM with Multihead Exponential Gated Fusion. Experiments on 3 benchmarks show that MEGA outperforms state-of-the-art baselines with superior accuracy and efficiency”

xLSTM for multivariate time series anomaly detection: arxiv.org/abs/2506.22837 “In our results, xLSTM showcases state-of-the-art accuracy, outperforming 23 popular anomaly detection baselines.” Again, xLSTM excels in time series analysis.

xLSTM for Human Action Segmentation: arxiv.org/abs/2506.09650 "HopaDIFF, leveraging a novel cross-input gate attentional xLSTM to enhance holistic-partial long-range reasoning" "HopaDIFF achieves state-of-theart results on RHAS133 in diverse evaluation settings."

Mein Buch “Was kann Künstliche Intelligenz?“ ist erschienen. Eine leicht zugängliche Einführung in das Thema Künstliche Intelligenz. LeserInnen – auch ohne technischen Hintergrund – wird erklärt, was KI eigentlich ist, welche Potenziale sie birgt und welche Auswirkungen sie hat.

We are soooo proud. Our European-developed TiRex is leading the field—significantly ahead of U.S. competitors like Amazon, Datadog, Salesforce, and Google, as well as Chinese models from companies such as Alibaba.

Attention!! Our TiRex time series model, built on xLSTM, is topping all major international leaderboards. A European-developed model is leading the field—significantly ahead of U.S. competitors like Amazon, Datadog, Salesforce, and Google, as well as Chinese models from Alibaba.

TiRex 🦖 time series xLSTM model ranked #1 on all leaderboards. ➡️ Outperforms models by Amazon, Google, Datadog, Salesforce, Alibaba ➡️ industrial applications ➡️ limited data ➡️ embedded AI and edge devices ➡️ Europe is leading Code: lnkd.in/eHXb-XwZ Paper: lnkd.in/e8e7xnri shorturl.at/jcQeq

Recommended read for the weekend: Sepp Hochreiter's book on AI! Lots of fun anecdotes and easily accessible basics on AI! www.beneventopublishing.com/ecowing/prod...

xLSTM for the classification of assembly tasks: arxiv.org/abs/2505.18012 "xLSTM model demonstrated better generalization capabilities to new operators. The results clearly show that for this type of classification, the xLSTM model offers a slight edge over Transformers."

Happy to introduce 🔥LaM-SLidE🔥! We show how trajectories of spatial dynamical systems can be modeled in latent space by --> leveraging IDENTIFIERS. 📚Paper: arxiv.org/abs/2502.12128 💻Code: github.com/ml-jku/LaM-S... 📝Blog: ml-jku.github.io/LaM-SLidE/ 1/n

1/11 Excited to present our latest work "Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics" at #ICLR2025 on Fri 25 Apr at 10 am! #CombinatorialOptimization #StatisticalPhysics #DiffusionModels

xLSTM for Multi-label ECG Classification: arxiv.org/abs/2504.16101 "This approach significantly improves ECG classification accuracy, thereby advancing clinical diagnostics and patient care." Cool.

Precall: 4 Tenure-Tracks in AI for females. Check it out.

Huge achievement: arxiv.org/abs/2503.14376 xLSTM kernels are now the fastest kernels both for training and inference. Faster than Flashattention or Mamba kernels. High arithmetic intensity. Optimized use of GPUs that set a new state of the art. Congratulations to the team. It is highly impressive.

Visited with legendary #AI pioneer @hochreitersepp.bsky.social at #JKULinz. We discussed the efficiency of #xLSTM against #GPT and why AI's future may be located on the edge of networks, rather than in centralised data centres via @bloomberg.com www.bloomberg.com/news/article...

xLSTM for Automated Stock Trading: arxiv.org/abs/2503.09655 xLSTM outperforms LSTM. "These findings mark the potential of xLSTM for enhancing DRL-based stock trading systems." This reinforcement learning approach uses xLSTM in both actor and critic components, which increases the performance.

Join Our Research Team in Linz! We are looking for 5 PostDocs and 10 PhDs in Machine Learning working on xLSTM, NLP, robustness, density ratio. Deadline: 04/20/25. More details: www.jku.at/en/lit-artif... #MachineLearning #DeepLearning #ResearchOpportunities #PhDPositions

Exploration imitation learning architectures: Transformer, Mamba, xLSTM: arxiv.org/abs/2502.12330 *LIBERO: “xLSTM shows great potential” *RoboCasa: “xLSTM models, we achieved success rate of 53.6%, compared to 40.0% of BC-Transformer” *Point Clouds: “xLSTM model achieves a 60.9% success rate”

xLSTM for time series with Granger causality: arxiv.org/abs/2502.09981 xLSTM again shows superb performance at time series analysis. "Our experimental evaluations on three datasets demonstrate the overall efficacy of our proposed GC-xLSTM model."

xLSTM shines at tumor segmentation: arxiv.org/abs/2502.00314 “evaluated state-of-the-art segmentation methods, including U-Net and its enhanced variants with Transformers and Mamba. Our proposed ViLU-Net [vision xLSTM-Net] model achieved superior performance with reduced complexity.” Cool.

Don't miss this workshop. Super interesting .

xLSTM for molecular property prediction: arxiv.org/abs/2501.18439 "AUROC improvement of 3.18% for classification tasks and an RMSE reduction of 3.83% across regression datasets compared to the baseline methods." Again xLSTM excels in a life science. Compare Bio-xLSTM: arxiv.org/abs/2411.04165.

xLSTM for knowledge tracing: arxiv.org/abs/2501.14256 xLSTM excels at another task. “DKT2 [the xLSTM] consistently outperforms 17 baseline models in various prediction tasks” Baseline models include Transformers, Mamba and graph-based methods. DKT2 exploits exponential gating and matrix memory.

EMBL & ELLIS deepen partnership to drive #AI in European #LifeSciences. 🤝 We aim to unlock new insights, train the next gen of AI researchers, & develop innovative solutions for human & planetary health. Read the full article here: ellis.eu/news/embl-an...

xLSTM outperforms Conformers (Transformers) and Mamba on Speech Enhancement: arxiv.org/abs/2501.06146 “xLSTM-SENet2, outperforms state-of-the-art Mamba- and Conformer-based systems on the Voicebank+DEMAND.” New xLSTM-Triton-Kernels are faster than FlashAttention 3 & Mamba for training and inference.

Often LLMs hallucinate because of semantic uncertainty due to missing factual training data. We propose a method to detect such uncertainties using only one generated output sequence. Super efficient method to detect hallucination in LLMs.