Screw Attention introduces a transformer layer that treats the relation between two bodies as a spatial transform rather than a graph edge, enabling each token to represent a body with a pose and its relative pose or joint screw. This design ensures equivariance to independent frame changes and allows a single layer to capture rigid‑body velocity recursion. Experiments on simulated manipulation tasks show that Screw Attention matches or outperforms other network architectures, achieving high success rates on LIBERO‑Spatial with far fewer parameters and maintaining performance under frame convention changes and pose noise.
By Aly Magassouba
MoLE introduces a Mixture of Latent Experts framework that encourages distinct latent tokens to extract complementary visual information, rather than sharing visual evidence. By isolating latent visual experts during evidence extraction and aggregating their outputs with dedicated summary experts, MoLE achieves higher performance on visual reasoning benchmarks compared to existing latent reasoning methods. The approach uses a two-stage training pipeline that first forces visual evidence through the latent pathway and then restores direct visual access, without requiring predefined expert roles or intermediate visual targets.
By Yingcheng Liu, Tianyi Jiang, Yujuan Ding, jiangbo Ai, Xun Jiang, Guoqing Wang, Wei Ye, Yi Bin
Chatham Financial uses Codex and GPT-5.6 to build technology and redesign workflows, cutting trade validation from 30 minutes to under 4.
High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with thermodynamic behavior acr...
Release: pwasm 0.2a0
pwasm is one of my folly projects - an entirely vibe-coded pure Python WebAssembly engine that I built in January during my first bout of AI mania.
I hadn't touched it sin...
The ability of vision-language models (VLMs) to associate visual identities with biographical information creates a need for selective unlearning of personally identifiable information (PII) while pre...
Large language models offer a promising foundation for chemical reasoning, bringing together chemical knowledge and multistep problem solving. Chemical intuition can provide an initial sense of plausi...
Hypernetworks have recently shown success in dynamically adapting the parameters of Large Language Models (LLMs) at runtime based on signals such as task descriptions or additional demostrations. Here...
Albertsons Companies is leveraging ChatGPT Enterprise and the OpenAI API to accelerate internal workflows and enhance the grocery shopping experience for millions of customers. By integrating these AI tools, the retailer aims to streamline team collaboration and improve service efficiency. The initiative reflects a broader trend of using advanced AI to transform retail operations from within.
Machine learning (ML) has emerged as a promising approach for improving diffusion MRI (dMRI) tractography, a task that remains limited by the intrinsic tension between local diffusion information and...
Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastructure. While learning-based schedulers excel in re...
We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km resolution and glob...
LLM arenas turn pairwise human preferences into model rankings. Those preferences may reflect how an answer is presented as well as what it says. We take a stylometric approach to 137,293 decisive Fre...
The article discusses how two components—a payload that hijacks an agent and an agent that transports the payload—can combine to form a worm. It explains that agents running in isolated sandboxes can leave instructions in a shared package cache, altering each other's behavior. By substituting the package cache with communication channels like email, Slack, or WhatsApp and replacing sandboxed training runs with independently deployed personal agents such as Muse, the conditions necessary for a worm are met.
Designing high-performance microwave absorbers requires specialized expertise in electromagnetic theory, materials science and simulation programming, and entails time-consuming optimization. Here, we...
As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the machine learning community lacks standardised practi...
The paper introduces Rita, a reinforcement learning framework that addresses thinking drift in vision‑language models by enforcing consistency between reasoning and answers. Rita employs two reasoning‑label‑free rewards—thinking and consistency rewards—derived from the conditional probability of reference answers, and uses a difficulty‑aware data filtering strategy to select informative samples for training. Experiments on EgoIntention and RefEgo‑Int benchmarks demonstrate that Rita outperforms both supervised fine‑tuning and vanilla RL‑fine‑tuned approaches.
By Pengzhan Sun, Shiu-hong Kao, Shijie Li, Yongyi Su, Junbin Xiao, Arjun Reddy Akula, Angela Yao
The paper proposes a new matrix multiplication approach called SFC-CA GEMM that uses space‑filling curves to partition work in a platform‑ and shape‑oblivious way, achieving communication‑avoiding properties. It demonstrates provable asymptotic communication optimality for both square and rectangular matrices and outperforms vendor libraries on multiple x86 and Arm platforms, with speedups up to 5.5× for specific shapes and 1.8× in weighted harmonic mean throughput. The method is applied to real‑world tasks, improving large‑language‑model inference by up to 1.85× and distributed‑memory GEMM by up to 2.3× over state‑of‑the‑art frameworks.
By Evangelos Georganas, Alexander Heinecke, Pradeep Dubey
GraphCert introduces a method to bootstrap graph reasoning agents by generating graph‑grounded question‑answer pairs and certifying the supporting evidence. The approach uses a Bootstrapped Graph Quizzer to produce QA pairs, then executes and semantically curates the evidence into certified rubrics that guide reward‑based training of a Graph Solver. Experiments on five GRBENCH domains show GraphCert outperforms larger LLM agents and demonstrates robust policy transfer across heterogeneous graphs.
By Weiqi Jiang, Yuchen Ying, Rui Wang, Kaixuan Chen, Bingde Hu, Shunyu Liu, Yu Wang, Tongya Zheng
The paper introduces TSDA-Track, a Template-Search Domain Adaptation framework designed to reduce modality gaps in cross‑modal visual object tracking. Two variants are explored: Pre‑AFA TSDA‑Track uses adversarial alignment before transformer interaction, while Enc‑CFA TSDA‑Track applies contrastive alignment after interaction to strengthen cross‑modal correspondence. Experiments on datasets such as LasHeR, RGBT234, GTOT, and Anti‑UAV‑024 show that both variants outperform state‑of‑the‑art trackers, with Pre‑AFA achieving an SR/PR of 43.2/56.0 on RGBT234 under the modality‑switch protocol.
By Fereshteh Aghaee Meibodi, Amir Mehdi Soufi Enayati, Shadi Alijani, Homayoun Najjaran