Chronological feed of everything captured from Arvind on AI.
paper / arvind-ai / 1d ago
This research presents a novel, unbinned, model-independent approach to precisely measure the CKM angle gamma. By jointly analyzing data from LHCb and BESIII experiments, the study combines charge-parity violating observables from B-meson decays with strong-phase parameters from D-meson decays. This methodology significantly improves the precision of the gamma angle determination, offering critical insights into CP violation within the Standard Model.
ckm-anglegamma-measurementcp-violationbesiii-experimentlhcb-experimentparticle-physicshigh-energy-physics
“The CKM angle gamma was measured using a novel, unbinned, model-independent approach.”
paper / arvind-ai / 1d ago
This paper introduces a coupled-cluster formalism utilizing imaginary-time evolution from an arbitrary reference. This method converges to standard coupled-cluster amplitude equations when finite solutions exist. Crucially, it provides additional information even when standard solutions are not available. The formalism also incorporates a coupled-cluster energy variance minimum to identify physically regularized coupled-cluster amplitudes.
quantum-chemistrycoupled-clustercomputational-physicsimaginary-time-evolutionchemical-physics
“A coupled-cluster formalism can perform imaginary-time evolution from an arbitrary reference.”
paper / arvind-ai / 1d ago
SHAPE is a novel framework that improves LLM reasoning by formalizing it as a state-space trajectory. It introduces a hierarchical credit assignment mechanism. This approach aims to distinguish meaningful progress from mere verbosity in process supervision, addressing limitations of existing methods in reasoning capability and token efficiency. SHAPE achieves better accuracy while reducing token consumption.
llm-reasoningreinforcement-learningprocess-supervisionnatural-language-processingllm-efficiencyai-research
“Existing process supervision methods for LLMs fail to distinguish meaningful progress from verbosity, leading to limited reasoning and token inefficiency.”
paper / arvind-ai / 1d ago
The Trial-and-Error Collection (TEC) dataset and platform capture detailed human problem-solving trajectories and reflections. This novel dataset reveals human superiority over LLMs in trial-and-error tasks, highlighting the need for more sophisticated AI techniques beyond simple heuristics. TEC provides a valuable resource for developing more capable AI systems by offering a foundation for understanding human trial-and-error behavior.
human-ai-interactiontrial-and-errorproblem-solvingai-datasetsllm-limitationshuman-cognition
“Existing AI techniques for trial-and-error are limited by reliance on simple heuristics and lack of appropriate data.”
paper / arvind-ai / 1d ago
Valve is a production-grade colocation system that optimizes GPU utilization by running offline workloads on idle capacity without compromising latency-critical online LLM inference. It employs a GPU runtime featuring channel-controlled compute isolation and page-fault-free memory reclamation to bound preemption latency and rate. The system demonstrates high scalability and low deployment friction, requiring negligible driver and framework modifications.
llm-inferenceresource-managementgpu-utilizationsystem-optimizationproduction-systemsoperating-systems
“Valve increases cluster utilization by 34.6%, resulting in a saving of 2,170 GPUs.”
youtube / arvind-ai / 1d ago
Despite significant investment and concerns about an "AI bubble," the fundamental utility and low inference costs of existing AI models suggest that AI adoption will persist even if a market correction occurs. The impact of a crash would likely be felt more in research and development funding rather than in the continued use and integration of established AI products into daily life and work.
ai-economicsmarket-analysisgenerative-aitech-industryai-investment
“Current AI investment, totaling over a trillion dollars in data centers, has not yet demonstrably boosted GDP growth, leading to speculation of an AI bubble.”
youtube / arvind-ai / 1d ago
Current narratives surrounding Artificial General Intelligence (AGI) often promote a sense of impending, transformative breakthrough, urging a "Manhattan Project" approach. This perspective, however, oversimplifies the complexities of AI development, misrepresents its potential impact, and carries significant political risks. AGI is unlikely to manifest as a sudden, observable event, and its integration into society will be gradual, necessitating a more nuanced and deliberate approach than an accelerated arms race.
agi-debatesai-policyai-risksai-benchmarkingtechnological-progresssocietal-impact-of-aiai-hype-cycles
“AGI is unlikely to be a sudden, observable event with immediate, earth-shattering consequences.”
youtube / arvind-ai / 1d ago
Moravec's Paradox, which posits that tasks difficult for humans are easy for AI and vice-versa, is a flawed framework for predicting AI capabilities. Its apparent validity stems from selective focus on specific AI research domains rather than an empirical truth about AI's inherent ease or difficulty with certain tasks. This misconception has led to both alarmism and false comfort regarding AI's societal impact, particularly concerning reasoning and robotics.
ai-predictionsmoravec-paradoxai-capabilitiesai-ethicstechnological-forecastingdeep-learningrobotics
“Moravec's Paradox is not empirically supported and offers no predictive power for AI advancements.”