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”

Tox21 leaderboard is now on huggingface. What is the best AI method for toxicity prediction: LLMs are not there. Toxicity prediction is essential in the first phase of drug discovery.

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.

Introducing TiRex - xLSTM based time series model | NXAI TiRex model at the top 🦖 We are proud of TiRex - our first time series model based on #xLSTM technology. Key take aways: 🥇 Ranked #1 on official international leaderboards ➡️ Outperforms models ...

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