Turnstile Raises $29M for AI-Driven Quote-to-Cash Platform
New platform delivers enterprise-grade revenue automation without the complexity Turnstile today launched its quote-to-cash platform for sales-led startups with $29 million in funding from First Round, OMERS Ventures, Illuminate Financial, and a number of prominent angel investors. When SaaS first e...
Cognizant, Palantir Partner for AI-Driven Enterprise Modernization
Partnership combines Palantir Foundry and AIP with Cognizant’s TriZetto healthcare platforms and business process operations to enable secure, scalable AI transformation for enterprises across industries. Cognizant (NASDAQ: CTSH) announced a strategic partnership with Palantir Technologies Inc. (NA...
Mendix appoints marketing leader Aviva Fink as Chief Growth Officer to drive global expansion Mendix, a Siemens business and global modern enterprise development platform, today announced the appointment of Aviva Fink as Chief Growth Officer (CGO). Fink joins the executive leadership team to oversee...
Denoising diffusion networks for normative modeling in neuroimaging
arXiv:2602.04886v1 Announce Type: new
Abstract: Normative modeling estimates reference distributions of biological measures conditional on covariates, enabling centiles and clinically interpretable deviation scores to be derived. Most neuroimaging pipelines fit one model per imaging-derived phenoty...
A Causal Perspective for Enhancing Jailbreak Attack and Defense
arXiv:2602.04893v1 Announce Type: new
Abstract: Uncovering the mechanisms behind "jailbreaks" in large language models (LLMs) is crucial for enhancing their safety and reliability, yet these mechanisms remain poorly understood. Existing studies predominantly analyze jailbreak prompts by probing lat...
Momentum Attention: The Physics of In-Context Learning and Spectral Forensics for Mechanistic Interpretability
arXiv:2602.04902v1 Announce Type: new
Abstract: The Mechanistic Interpretability (MI) program has mapped the Transformer as a precise computational graph. We extend this graph with a conservation law and time-varying AC dynamics, viewing it as a physical circuit. We introduce Momentum Attention, a ...
Mind the Performance Gap: Capability-Behavior Trade-offs in Feature Steering
arXiv:2602.04903v1 Announce Type: new
Abstract: Feature steering has emerged as a promising approach for controlling LLM behavior through direct manipulation of internal representations, offering advantages over prompt engineering. However, its practical effectiveness in real-world applications rem...
DCER: Dual-Stage Compression and Energy-Based Reconstruction
arXiv:2602.04904v1 Announce Type: new
Abstract: Multimodal fusion faces two robustness challenges: noisy inputs degrade representation quality, and missing modalities cause prediction failures. We propose DCER, a
unified framework addressing both challenges through dual-stage compression and ener...
Artificial Intelligence as Strange Intelligence: Against Linear Models of Intelligence
arXiv:2602.04986v1 Announce Type: new
Abstract: We endorse and expand upon Susan Schneider's critique of the linear model of AI progress and introduce two novel concepts: "familiar intelligence" and "strange intelligence". AI intelligence is likely to be strange intelligence, defying familiar patte...
DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search
arXiv:2602.05014v1 Announce Type: new
Abstract: With the rapid progress of tool-using and agentic large language models (LLMs), Retrieval-Augmented Generation (RAG) is evolving from one-shot, passive retrieval into multi-turn, decision-driven evidence acquisition. Despite strong results in open-dom...
Evaluating Large Language Models on Solved and Unsolved Problems in Graph Theory: Implications for Computing Education
arXiv:2602.05059v1 Announce Type: new
Abstract: Large Language Models are increasingly used by students to explore advanced material in computer science, including graph theory. As these tools become integrated into undergraduate and graduate coursework, it is important to understand how reliably t...
Towards Reducible Uncertainty Modeling for Reliable Large Language Model Agents
arXiv:2602.05073v1 Announce Type: new
Abstract: Uncertainty quantification (UQ) for large language models (LLMs) is a key building block for safety guardrails of daily LLM applications. Yet, even as LLM agents are increasingly deployed in highly complex tasks, most UQ research still centers on sing...
Anthropic Releases Claude Opus 4.6 With 1M Context, Agentic Coding, Adaptive Reasoning Controls, and Expanded Safety Tooling Capabilities
Anthropic has launched Claude Opus 4.6, its most capable model to date, focused on long-context reasoning, agentic coding, and high-value knowledge work. The model builds on Claude Opus 4.5 and is now available on claude.ai, the Claude API, and major cloud providers under the ID claude-opus-4-6. Mod...
OpenAI Just Launched GPT-5.3-Codex: A Faster Agentic Coding Model Unifying Frontier Code Performance And Professional Reasoning Into One System
OpenAI has just introduced GPT-5.3-Codex, a new agentic coding model that extends Codex from writing and reviewing code to handling a broad range of work on a computer. The model combines the frontier coding performance of GPT-5.2-Codex with the reasoning and professional knowledge capabilities of G...
Natively Adaptive Interfaces: A new framework for AI accessibility
A collage of four images, the first of a woman with curly hair in front of a silver laptop, the second of the same woman and a man with short black hair speaking on a stairwell, the third of a the same man with glasses, and an aerial image of NTID
Rethinking imitation learning with Predictive Inverse Dynamics Models
This research looks at why Predictive Inverse Dynamics Models often outperform standard Behavior Cloning in imitation learning. By using simple predictions of what happens next, PIDMs reduce ambiguity and learn from far fewer demonstrations.
The post Rethinking imitation learning with Predictive Inv...
Fundamental raises $255 million Series A with a new take on big data analysis
Fundamental has built a new foundation model to solve an old problem: how to draw insights from the huge quantities of structured data produced by enterprises.