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SELFDOUBT is a new framework for uncertainty quantification in reasoning language models. It addresses the difficulty of deploying uncertainty estimation in practice, particularly for proprietary APIs.
ReVEL is a hybrid framework that uses large language models for iterative reasoning in combinatorial optimization. It embeds LLMs within an evolutionary algorithm to improve heuristic design.
Researchers challenge the notion that supervised finetuning memorizes while reinforcement learning generalizes. They find that cross-domain generalization is conditional, influenced by optimization, data, and model capability. This challenges prevailing narratives in LLM post-training.
Large reasoning models perform well on multi-step tasks but have unstable behavior. Step-Saliency analysis reveals information-flow failures.
Quantum computers can break vital encryption with fewer resources than thought. This increases the threat to elliptic curve cryptosystems.
ProofSketcher combines LLMs with a lightweight proof checker for reliable math and logic reasoning. It aims to address the limitations of LLMs in producing persuasive but flawed arguments.
Pramana is a novel approach to fine-tune large language models for epistemic reasoning. It aims to address the epistemic gap in AI, where models struggle with systematic reasoning and often produce unfounded claims.
Pramana is a novel approach that teaches large language models explicit epistemological methods to improve their reasoning. This approach aims to address the epistemic gap in AI, where models struggle with systematic reasoning and often produce unfounded claims.
Pramana is a novel approach to fine-tune large language models for epistemic reasoning. It aims to address the epistemic gap in LLMs, enabling them to ground claims in traceable evidence.
Pramana is a novel approach to fine-tune large language models for epistemic reasoning. It aims to address the epistemic gap in LLMs, enabling them to ground claims in traceable evidence.
PaperOrchestra is a multi-agent framework that automates AI research paper writing. It transforms unstructured materials into submission-ready manuscripts, including literature synthesis and generated visuals.
PaperOrchestra is a multi-agent framework for automated AI research paper writing. It transforms pre-writing materials into submission-ready manuscripts, including literature synthesis and generated visuals.
OpenAI secures $122 billion in funding to expand AI globally. The investment will boost next-gen compute and meet growing demand for ChatGPT and enterprise AI.
New attacks exploit GPU memory to hijack CPUs. GDDRHammer, GeForge, and GPUBreach give attackers complete control.
Researchers introduce a framework to study operational noncommutativity in sequential metacognitive judgments. This work explores how order effects impact cognitive processes.
Researchers trained mRNA language models across 25 species for $165. This breakthrough has significant implications for bioinformatics and natural language processing.
MMORF is a framework for designing multi-objective retrosynthesis planning systems. It leverages language model-based multi-agent systems to balance quality, safety, and cost objectives.
MMORF is a framework for designing multi-objective retrosynthesis planning systems. It leverages language model-based multi-agent systems to balance quality, safety, and cost objectives.
MMORF is a framework for designing multi-objective retrosynthesis planning systems. It leverages language model-based multi-agent systems to balance quality, safety, and cost objectives.
Meta is reentering the AI race with Muse Spark, its first new model since overhauling AI efforts. The model will power various Meta platforms.