SelfEvolve achieves 92.7% Pass@1 success on 11 runtime self-extension tasks and outperforms baselines like AutoGen by 61.8% with statistical significance.
2024.2024 Generative AI Red Teaming Transparency Report
7 Pith papers cite this work. Polarity classification is still indexing.
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Constrained decoding derived from OpenAPI specifications eliminates hallucinated web API calls in LLM-generated code and substantially improves correctness across 24 models, while retrieval-augmented generation yields inconsistent, model-dependent gains.
A participatory red-teaming project in the Global South created the PLACES dataset of 26k T2I failure examples that reveal unique cultural and linguistic harms missed by existing safety frameworks.
EasyRider uses passive components plus actively controlled energy storage at the rack level, paired with lifetime-maximizing software, to keep AI training power transients inside grid safety limits without code changes or energy waste.
AI data centers disrupt grid load diversity with rapid hundreds-of-MW swings, requiring explicit co-design between compute and power systems instead of implicit coexistence.
AI-native software ecosystems exhibit emergent behaviors best explained by complex adaptive systems theory, requiring new ecosystem-level monitoring and seven testable propositions that may extend or replace Lehman's laws.
Hiding generative AI use to signal expertise reduces knowledge sharing and transparency among workplace colleagues.
citing papers explorer
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Software Self-Extension with SelfEvolve: an Agentic Architecture for Runtime Code Generation
SelfEvolve achieves 92.7% Pass@1 success on 11 runtime self-extension tasks and outperforms baselines like AutoGen by 61.8% with statistical significance.
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Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding
Constrained decoding derived from OpenAPI specifications eliminates hallucinated web API calls in LLM-generated code and substantially improves correctness across 24 models, while retrieval-augmented generation yields inconsistent, model-dependent gains.
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Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South
A participatory red-teaming project in the Global South created the PLACES dataset of 26k T2I failure examples that reveal unique cultural and linguistic harms missed by existing safety frameworks.
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EasyRider: Mitigating Power Transients in Datacenter-Scale Training Workloads
EasyRider uses passive components plus actively controlled energy storage at the rack level, paired with lifetime-maximizing software, to keep AI training power transients inside grid safety limits without code changes or energy waste.
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From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design
AI data centers disrupt grid load diversity with rapid hundreds-of-MW swings, requiring explicit co-design between compute and power systems instead of implicit coexistence.
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More Is Different: Toward a Theory of Emergence in AI-Native Software Ecosystems
AI-native software ecosystems exhibit emergent behaviors best explained by complex adaptive systems theory, requiring new ecosystem-level monitoring and seven testable propositions that may extend or replace Lehman's laws.
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"If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace
Hiding generative AI use to signal expertise reduces knowledge sharing and transparency among workplace colleagues.