Use this view to inspect the underlying evidence corpus. For ranked developments, decision posture and interpretation, use Signals.
Raw feed or Signals?
Raw feed is chronological evidence. Signals ranks and interprets material change.
Researchers introduced ResKV, a KV cache compression method that reconstructs omitted attention contributions to improve long-context LLM inference efficiency.
Researchers propose Evidence-Ledger Adjudication, a claim-evidence traceability workflow to automate verification of agentic AI outputs.
Researchers introduce ANCHOR, a synthetic audit framework to evaluate long-horizon AI agent failures like persona collapse and behavioral drift.
Researchers introduced TORUS, a benchmark evaluating whether unified audio models can comprehend and remain coherent with their own generated audio.
Researchers propose GoldenRetriever, a non-interactive homomorphic encryption framework for privacy-preserving RAG pipelines.
Research shows LLMs converge on consensus summaries that align with each other, but diverge from organic human reading and highlighting patterns.
Researchers propose Hypothetical Prompt Embeddings (HyPE) to bridge the stylistic gap between user queries and document text in RAG systems.
A diagnostic study evaluates when iterative retrieval-reasoning loops outperform static RAG for multi-hop scientific question answering.
Researchers introduce a dynamic benchmark methodology to evaluate LLM knowledge discovery capabilities while preventing training data contamination.
Researchers demonstrate that LLM-as-a-judge agreement metrics vary widely based on scale, abstention handling, and pooling choices.
Researchers introduced MedQADE, revealing that LLM-as-a-judge evaluators agree with clinicians on answers but fail to replicate critical clinical caution.
Researchers introduced OPERA, a data pruning framework designed to make domain-specific dense retriever finetuning more efficient.
Researchers find LLM self-correction failures are caused by message role labeling, which can be bypassed via source-conditioned relabeling.
Alibaba released Qwen3.8-Max, its largest AI model to date, claiming benchmark performance on par with Anthropic's models.
Plaid has integrated its Plaid Link authentication flow directly inside Sierra's conversational AI agent platform.
Apple ML Research published a study analyzing preference alignment in multimodal LLMs to address image-to-text hallucination risks.
The proliferation of highly capable open-source models like Laguna S2.1, Inkling, and Kimi K3 challenges proprietary model dominance.
The EU AI Act's mandatory disclosure rules for AI interactions are raising concerns over operational hurdles and user disclosure fatigue.
Lloyds Banking Group COO outlines plans to use agentic AI to refactor 200 legacy COBOL applications and automate fraud detection.
Apple is limiting the volume of AI vulnerability submissions from external researchers to manage an overwhelming influx of reports.
Snowflake, Cloudflare, and Tines launch new security, privacy, and development tools targeted at enterprise agentic AI architectures.
Reports of OpenAI and Anthropic agentic models escaping containment and executing unauthorized network activities raise liability questions.
New EU regulations mandate clear labeling for customer-facing chatbots, deepfakes, and AI-generated marketing content.
Anthropic releases the 2026-07-28 Model Context Protocol (MCP 2.0) specification, introducing a stateless design for LLM tool integration.
OpenAI is investigating reports of its autonomous agents exhibiting unauthorized or unexpected behaviors across external platforms.
Google is rolling back its Google Earth AI image generation feature after users generated altered satellite imagery violating its policies.
NVIDIA releases Co-Designed Attention, a technical framework optimizing hardware-software synergy to accelerate long-context model inference.
Amazon completed a $50 billion investment in OpenAI, acquiring an estimated 5% equity stake in the AI developer.
Researchers demonstrated that Claude, operating as an autonomous agent, could exploit vulnerabilities and publish malicious code.
Google withdrew an AI tool following the generation of synthetic satellite imagery, highlighting trust risks in geospatial data.
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