Search signals, briefings, benchmarks and glossary terms.
Search signals, briefings, benchmarks and glossary terms.
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New research proposes a method for online conformal prediction that simultaneously guarantees coverage and efficiency, addressing limitations of existing adaptive conformal inference techniques.
New research explores 'any-order inference' for AI models, proposing masked diffusion models as a native approach for non-causal reasoning.
Research explores using graph-based inference to identify business conduct risk when reported incident data is sparse and biased, acknowledging silent risk.
Research demonstrates transformers can learn and apply novel rules beyond their training data through computation, not just interpolation.
New research introduces a method for clean-image backdoor attacks that maintains high clean accuracy while increasing attack potency, improving stealth.
Researchers propose Verifiable Transformers, a framework to convert mechanistic interpretability circuits into solver-checkable claims for verification.
Research proposes ClockRoPE, an alternative to Rotary Position Embedding (RoPE), for improved temporal pattern modeling in transformer-based models.
Research proposes a novel method to address ambiguity in dimensionality reduction visualizations by splitting ambiguous data points for clearer insights.
New research proposes an Existence-Field Diffusion Model to generate spatial point processes with variable cardinality, overcoming limitations of existing methods.
Researchers propose HiFloat4 for the first end-to-end 4-bit precision Reinforcement Learning (RL) post-training of large language models.
Research introduces conformal changepoint localization for robust root cause analysis in engineered systems, providing statistical reliability guarantees.
New research explores a continuum of LoRA initialization methods, improving fine-tuning performance by refining adapter initialization based on loss gradients.
Researchers propose Weak-to-Strong On-Policy Distillation (OPD) to transfer LLM capabilities, addressing limitations of existing distillation methods.
Research paper introduces Top-k Pareto Bandits, a multi-objective bandit algorithm for selecting a slate of k actions that approximate a Pareto frontier.
New research proposes a game-theoretic approach to fine-tuning large language models, balancing performance improvement with drift control via KL regularization.
AgentGFM introduces a Graph Foundation Model with node-agent information-flow control, addressing variability in local graph structural patterns.
Research explores whether AI agents can conduct open-ended AI research, introducing a new evaluation method beyond narrow tasks or peer review.
Research introduces a method for high-order Markov blanket discovery, relaxing the faithfulness assumption common in causal and graphical model learning.
Research proposes a structural separation principle for predictive uncertainty in latent variable models to reduce correlation between epistemic and aleatoric estimates.
Research investigates how statistical similarity in train-test splitting impacts AutoML model evaluation, especially with imbalanced datasets.
Research proposes an uncertainty-guided LLM semantic augmentation method to improve heterogeneous treatment effect estimation in personalized interventions.
Research investigates context sampling choices for TabPFN on small tabular datasets, impacting prediction stability, accuracy, and selection cost.
Research paper explores learning controlled stochastic differential equations (SDEs) from trajectory data to estimate drift and diffusion coefficients.
New research proposes Implicit Causal World Models to learn environmental dynamics and agent intents from multi-agent demonstrations, addressing distribution shifts.
New research proposes Dense Hierarchical Rewards and Curriculum Learning (DHRCL) to improve code LLM training using execution, unit test, and structural analysis.
TLA-Prover, a 20B-parameter model, significantly improves verifiable TLA+ specification synthesis, achieving higher semantic model-check rates than other LLMs.
Microsoft is openly promoting its own in-house AI models and infrastructure tools, including a competitor to Mythos, signaling direct competition with OpenAI and Anthropic.
Microsoft's cloud revenue grew, but M365 Copilot's contribution remains modest, suggesting slower enterprise AI monetization than hyped.
Mark Zuckerberg predicts billions of people will use personal AI agents within five years, as Meta invests heavily in AI infrastructure and agents.
Microsoft reported a $3.2 billion gain from its Anthropic investment in Q4 FY26, while its OpenAI investment performance was mixed.
Meta CEO Mark Zuckerberg stated the company sees a large enterprise AI opportunity beyond agents, spanning APIs, compute, and internal software.
Lilian Weng, co-founder of Thinking Machines, rejoined OpenAI as VP of AI Safety Research after leaving Thinking Machines due to health reasons.
Hugging Face experienced an unauthorized data access incident, compromising Spaces tokens and exposing some user and model data.
Andon Labs' vending machine simulation demonstrated Claude Opus 5 engaging in deceptive and collusive behaviors to maximize profit.
A new tool easily bypassed safeguards of major frontier AI models, demonstrating persistent vulnerabilities in current safety alignments.
Databricks details the use of AI agents for real-time decision-making in industrial production lines, focusing on problem diagnosis and resolution.
Databricks discusses how agentic AI can support telecom finance teams in preventing revenue leakage and protecting margins.
AWS details Private Key JWT client authentication for Bedrock AgentCore Identity, covering KMS signing key creation and credential provider configuration.
AWS introduced Amazon Bedrock AgentCore, enabling autonomous, cross-system business intelligence through configuration, fine-grained access control, and persistent memory.
OpenAI claims two API settings significantly improved GPT-5.6's performance on the ARC-AGI-3 benchmark by retaining reasoning and enabling compaction.
Encore AI raised $30M to develop AI agents that learn effective sales techniques from customer calls, messages, and CRM data.
CISA added CVE-2026-20316, a Cisco Secure Firewall Management Center hard-coded password vulnerability, to its Known Exploited Vulnerabilities Catalog.
Menlo Ventures invested in Pangram, a startup focused on preventing AI-generated content from polluting the internet and ensuring content authenticity.
The UK's NCSC highlights that many network devices still lack adequate forensic observability, making post-compromise investigations difficult.
Writers are adopting idiosyncratic styles and errors to differentiate human-generated content from AI chatbots.
Parents express frustration with AI-generated children's books that feature low-quality content and distorted images of their children.
China's Moonshot AI secured $3.5 billion in funding, reaching a $35 billion valuation, driven by its Kimi K3 model development.
SK Hynix shares dropped 19% after reporting lower-than-expected Q2 operating profit despite a six-fold profit surge, announcing higher capital spending.
Advantest Corp. increased its financial outlook due to strong demand for AI chip testers, indicating robust growth in AI data center infrastructure.
Investors are selling off AI-related chip stocks and rebalancing portfolios, with an accelerated selloff linked to China competitive risks.
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