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 introduce AntiSkillBench to evaluate privacy leakage and impersonation risks in agentic AI systems utilizing persona skills.
Researchers propose a diagnostic to measure whether diverse outputs in LLM collectives actually indicate a capacity for decision revision.
Researchers identify structured planning and grounding failure modes in multilingual multi-agent systems during non-English task execution.
Researchers proposed a new benchmark evaluating LLMs across the entire database lifecycle, shifting focus beyond simple Text-to-SQL tasks.
Researchers find LLMs develop implicit internal state representations to perform complex mental arithmetic without generating intermediate text.
Researchers analyze why discrete optimization-based adversarial suffixes (jailbreaks) transfer successfully across different LLMs.
Researchers propose Self-Guided Adaptive Safety Alignment (SGASA) to help reasoning models synthesize and internalize safety guidelines.
Researchers develop a methodology to quantify LLM hallucinations by evaluating question-answering performance against fixed textbook sources.
Researchers identify 'word recovery' as the mechanism enabling LLMs to process and reconstruct noisy or character-level tokenized inputs.
An academic paper challenges mechanistic interpretability, arguing that LLM meaning is context-dependent and cannot map to fixed circuits.
An academic analysis challenges the claim that latent continuous chain-of-thought models leverage superposition to hold multiple solutions.
Research demonstrates speculative decoding for LLM inference acceleration is significantly less effective in non-English languages.
Researchers propose segment-level credit assignment to reduce costly and unproductive 'overthinking' in reasoning language models.
Researchers analyzed 20,000 real-world mental health AI conversations to evaluate safety alignment against simulated benchmarks.
Researchers introduced LogitScope, a lightweight framework that analyzes LLM uncertainty using token-level probability distribution metrics.
Researchers introduced CaliDist, a post-hoc LLM calibration method that penalizes confidence scores based on model vulnerability to distraction.
Research demonstrates that standard behavioral safety audits fail to detect how easily an LLM can be bypassed via minor representation-level changes.
US drafts import ban on Chinese optical data center components, causing Chinese optical networking stocks to slide amid AI infrastructure security concerns.
CIBC has launched an AI-powered platform to automate administrative tasks for its financial advisors, aiming to increase client-facing time.
State Street analysis challenges the direct link between AI-driven productivity gains and macroeconomic disinflation.
State Street analyzes why the transition from AI-driven productivity gains to broader economic disinflation is complex and indirect.
State Street analyzes the complex transmission channels between AI productivity gains, macroeconomic inflation, and interest rates.
State Street analyzes how AI-driven productivity gains contribute to macroeconomic disinflation, framing the cost-reduction potential of enterprise AI.
State Street published analysis on how AI-driven productivity gains could exert structural disinflationary pressures on financial services.
State Street outlines how AI-driven productivity gains are starting to exert disinflationary pressure on structural operating costs.
State Street analyzes how AI-driven productivity gains could contribute to structural disinflation by lowering corporate operational costs.
State Street released research analyzing how AI-driven productivity gains will contribute to macroeconomic disinflation.
State Street published research on how AI-driven productivity gains will create structural disinflationary pressures across the economy.
State Street publishes research analyzing how AI-driven productivity gains will exert downward pressure on global inflation rates.
State Street issued research analyzing how AI-driven productivity gains could contribute to macroeconomic disinflation.
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