Unveiling a Hidden Percolation Transition in Monitored Clifford Circuits: Inroads from ZX-Calculus

Posted by klaurent Cornell

Kavli Affiliate: Debanjan Chowdhury | Summary:We revisit the measurement-induced phase transition (MPT) in Clifford circuits, which are both classically simulable and exhibit critical behavior widely believed to be distinct from classical percolation theory, using ZX-calculus. We analyze the MPT in a dynamical model composed of CNOT, SWAP, identity gates, and Bell-pair measurements, respectively, arranged randomly […]


Continue.. Unveiling a Hidden Percolation Transition in Monitored Clifford Circuits: Inroads from ZX-Calculus

High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion

Posted by dbos Berkeley

Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Yang Zhang, Lukas Mehl, Markus Gross, Christopher Schroers | Summary: Despite recent advances in Novel View Synthesis (NVS), generating high-fidelity views from single or sparse observations remains a significant challenge. Existing splatting-based approaches often produce distorted geometry due to splatting errors. While diffusion-based methods leverage […]


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SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning

Posted by klaurent Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Junkai Chen, Junkai Chen, , , | Summary: As Multimodal Large Language Models (MLLMs) develop, their potential security issues have become increasingly prominent. Machine Unlearning (MU), as an effective strategy for forgetting specific knowledge in training data, has been widely used in privacy protection. However, MU for […]


Continue.. SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning

SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning

Posted by klaurent Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Junkai Chen, Junkai Chen, , , | Summary: As Multimodal Large Language Models (MLLMs) develop, their potential security issues have become increasingly prominent. Machine Unlearning (MU), as an effective strategy for forgetting specific knowledge in training data, has been widely used in privacy protection. However, MU for […]


Continue.. SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning

SAFEERASER: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning

Posted by dbos Tokyo

Kavli Affiliate: Jia Liu | First 5 Authors: Junkai Chen, Zhijie Deng, Kening Zheng, Yibo Yan, Shuliang Liu | Summary: As Multimodal Large Language Models (MLLMs) develop, their potential security issues have become increasingly prominent. Machine Unlearning (MU), as an effective strategy for forgetting specific knowledge in training data, has been widely used in privacy […]


Continue.. SAFEERASER: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning

EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning

Posted by klaurent Peking

Kavli Affiliate: Ke Wang | First 5 Authors: Xiaoqian Liu, Ke Wang, Yongbin Li, Yuchuan Wu, Wentao Ma | Summary: Large Language Models (LLMs) have shown impressive reasoning capabilities in well-defined problems with clear solutions, such as mathematics and coding. However, they still struggle with complex real-world scenarios like business negotiations, which require strategic reasoning-an […]


Continue.. EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning

EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning

Posted by dbos Peking

Kavli Affiliate: Ke Wang | First 5 Authors: Xiaoqian Liu, Ke Wang, Yongbin Li, Yuchuan Wu, Wentao Ma | Summary: Large Language Models (LLMs) have shown impressive reasoning capabilities in well-defined problems with clear solutions, such as mathematics and coding. However, they still struggle with complex real-world scenarios like business negotiations, which require strategic reasoning-an […]


Continue.. EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning

EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning

Posted by dbos Peking

Kavli Affiliate: Ke Wang | First 5 Authors: Xiaoqian Liu, Ke Wang, Yongbin Li, Yuchuan Wu, Wentao Ma | Summary: Large Language Models (LLMs) have shown impressive reasoning capabilities in well-defined problems with clear solutions, such as mathematics and coding. However, they still struggle with complex real-world scenarios like business negotiations, which require strategic reasoning-an […]


Continue.. EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning

EquiBench: Benchmarking Code Reasoning Capabilities of Large Language Models via Equivalence Checking

Posted by dbos Peking

Kavli Affiliate: Ke Wang | First 5 Authors: Anjiang Wei, Jiannan Cao, Ran Li, Hongyu Chen, Yuhui Zhang | Summary: Equivalence checking, i.e., determining whether two programs produce identical outputs for all possible inputs, underpins a broad range of applications, including software refactoring, testing, and optimization. We present the task of equivalence checking as a […]


Continue.. EquiBench: Benchmarking Code Reasoning Capabilities of Large Language Models via Equivalence Checking

EquiBench: Benchmarking Code Reasoning Capabilities of Large Language Models via Equivalence Checking

Posted by dbos Peking

Kavli Affiliate: Ke Wang | First 5 Authors: Anjiang Wei, Jiannan Cao, Ran Li, Hongyu Chen, Yuhui Zhang | Summary: Equivalence checking, i.e., determining whether two programs produce identical outputs for all possible inputs, underpins a broad range of applications, including software refactoring, testing, and optimization. We present the task of equivalence checking as a […]


Continue.. EquiBench: Benchmarking Code Reasoning Capabilities of Large Language Models via Equivalence Checking