Muon+: Towards Better Muon via One Additional Normalization Step
arXiv:2602.21545v1 Announce Type: new Abstract: The Muon optimizer has demonstrated promising performance in pre-training large language models through gradient (or momentum) orthogonalization. In this work, we propose a simple yet effective enhancement to Muon, namely Muon+, which introduces an additional...
Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction
arXiv:2602.21550v1 Announce Type: new Abstract: Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target genes from hundreds of...
Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip
arXiv:2602.21601v1 Announce Type: new Abstract: High stress occurs when 3D heterogeneous IC packages are subjected to thermal cycling at extreme temperatures. Stress mainly occurs at the interface between different materials. We investigate stress image using latent space representation which is...
AI’s Future May Be Quantum
Stephanie Seoyun Hwang, J.D. Class of 2028 While most people recognize AI as a transformative force, fewer are aware of one of the key technologies fueling its progress: quantum computing. In fact, many governments and tech industry actors see it...
xAI spent $7M building wall that barely muffles annoying power plant noise
“Temu sound wall” not enough to quell fury over xAI’s power plant.
Jack Dorsey just halved the size of Block’s employee base — and he says your company is next
Jack Dorsey has long been an open admirer of Elon Musk. Now, it seems, he may have been taking notes.
Anthropic CEO stands firm as Pentagon deadline looms
Anthropic CEO Dario Amodei said Thursday that he "cannot in good conscience accede" to the Pentagon's demands to give the military unrestricted access to its AI systems.
So, we’re getting Prada Meta AI glasses, right?
Mark Zuckerberg was at Prada's fashion week event in Milan, leaving everyone to wonder if we're getting Meta AI glasses under the Prada brand.
Sophia Space raises $10M seed to demo novel space computers
The company's modular computer tiles offer a new vision for space data centers.
Mistral AI inks a deal with global consulting giant Accenture
Mistral AI lands a partnership with Accenture, the consultant that has also recently announced partnerships with rivals OpenAI and Anthropic.
Read AI launches an email-based ‘digital twin’ to help you with schedules and answers
Read AI is launching Ada, which can reply with your availability and extract answers from the company knowledge base and the web.
Bumble adds AI-powered photo feedback and profile guidance tools
Bumble and other popular dating apps, like Match Group's Tinder and Hinge, have all embraced AI-powered features.
Google launches Nano Banana 2 model with faster image generation
Google is making Nano Banana 2 a default model in Gemini app and in AI mode.
Exhibit in Boston’s startup ecosystem at TechCrunch Founder Summit 2026
On June 9, over 1,000 founders, investors, and decision-makers will gather for TechCrunch Founder Summit 2026. This isn’t just foot traffic. It’s a full day of concentrated deal flow.
Figma partners with OpenAI to bake in support for Codex
Figma is integrating OpenAI's coding assistant Codex a week after it announced a similar integration with Anthropic's Claude Code.
Trace raises $3M to solve the AI agent adoption problem in enterprise
Trace is launching with $3 million in seed funding, including investment from Y Combinator, Zeno Ventures, Transpose Platform Management, Goodwater Capital, Formosa Capital, and WeFunder.
Semantic Novelty at Scale: Narrative Shape Taxonomy and Readership Prediction in 28,606 Books
arXiv:2602.20647v1 Announce Type: new Abstract: I introduce semantic novelty--cosine distance between each paragraph's sentence embedding and the running centroid of all preceding paragraphs--as an information-theoretic measure of narrative structure at corpus scale. Applying it to 28,606 books in PG19 (pre-1920...
CAMEL: Confidence-Gated Reflection for Reward Modeling
arXiv:2602.20670v1 Announce Type: new Abstract: Reward models play a fundamental role in aligning large language models with human preferences. Existing methods predominantly follow two paradigms: scalar discriminative preference models, which are efficient but lack interpretability, and generative judging models, which...
Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization
arXiv:2602.20743v1 Announce Type: new Abstract: Anonymizing textual documents is a highly context-sensitive problem: the appropriate balance between privacy protection and utility preservation varies with the data domain, privacy objectives, and downstream application. However, existing anonymization methods rely on static, manually...
Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
arXiv:2602.20816v1 Announce Type: new Abstract: The core learning signal used in language model distillation is the standard Kullback-Leibler (KL) divergence between the student and teacher distributions. Traditional KL divergence tends to be dominated by the next tokens with the highest...
FinAnchor: Aligned Multi-Model Representations for Financial Prediction
arXiv:2602.20859v1 Announce Type: new Abstract: Financial prediction from long documents involves significant challenges, as actionable signals are often sparse and obscured by noise, and the optimal LLM for generating embeddings varies across tasks and time periods. In this paper, we...
Linear Reasoning vs. Proof by Cases: Obstacles for Large Language Models in FOL Problem Solving
arXiv:2602.20973v1 Announce Type: new Abstract: To comprehensively evaluate the mathematical reasoning capabilities of Large Language Models (LLMs), researchers have introduced abundant mathematical reasoning datasets. However, most existing datasets primarily focus on linear reasoning, neglecting other parts such as proof by...
Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning
arXiv:2602.21103v1 Announce Type: new Abstract: Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational overhead. To...
MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation
arXiv:2602.20423v1 Announce Type: cross Abstract: Medical image segmentation remains challenging due to limited annotations for training, ambiguous anatomical features, and domain shifts. While vision-language models such as CLIP offer strong cross-modal representations, their potential for dense, text-guided medical image segmentation...
Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference
arXiv:2602.20449v1 Announce Type: cross Abstract: Modern Protein Language Models (PLMs) apply transformer-based model architectures from natural language processing to biological sequences, predicting a variety of protein functions and properties. However, protein language has key differences from natural language, such as...
GATES: Self-Distillation under Privileged Context with Consensus Gating
arXiv:2602.20574v1 Announce Type: cross Abstract: We study self-distillation in settings where supervision is unreliable: there are no ground truth labels, verifiable rewards, or external graders to evaluate answers. We focus on document-grounded question answering with asymmetric context, where a single...
Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures
arXiv:2602.20994v1 Announce Type: cross Abstract: Report-supervised (RSuper) learning seeks to alleviate the need for dense tumor voxel labels with constraints derived from radiology reports (e.g., volumes, counts, sizes, locations). In MRI studies of brain tumors, however, we often involve multi-parametric...
Model Merging in the Essential Subspace
arXiv:2602.20208v1 Announce Type: new Abstract: Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model without additional training. Despite extensive research, task interference remains a major obstacle that often undermines...
Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling
arXiv:2602.20210v1 Announce Type: new Abstract: Crystal modeling spans a family of conditional and unconditional generation tasks across different modalities, including crystal structure prediction (CSP) and \emph{de novo} generation (DNG). While recent deep generative models have shown promising performance, they remain...
MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning
arXiv:2602.20223v1 Announce Type: new Abstract: Recently, TabPFN has gained attention as a foundation model for tabular data. However, it struggles to integrate heterogeneous modalities such as images and text, which are common in domains like healthcare and marketing, thereby limiting...