TClone introduces low-latency forking of live GUI environments via sibling containers, copy-on-write sharing, and asynchronous checkpointing, achieving 1.9x and 1.5x lower task latency than KVM and CRIU.
Griffiths, Yuan Cao, and Karthik Narasimhan
4 Pith papers cite this work. Polarity classification is still indexing.
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MAP-Law dynamically controls retrieval depth in legal AI by computing element coverage, evidence coverage, and marginal gain on a joint node graph, reaching 0.86 element coverage with 58% fewer rounds than fixed baselines on 50 labor-law cases.
Solvita is an agentic evolution system using Planner, Solver, Oracle, and Hacker agents with trainable graph knowledge networks updated by reinforcement learning on pass/fail and vulnerability signals to achieve SOTA code generation performance.
ProactAgent treats memory retrieval as a learned policy action and reports large gains in success rate and fewer interaction rounds on SciWorld, AlfWorld, and StuLife.
citing papers explorer
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TClone: Low-Latency Forking of Live GUI Environments for Computer-Use Agents
TClone introduces low-latency forking of live GUI environments via sibling containers, copy-on-write sharing, and asynchronous checkpointing, achieving 1.9x and 1.5x lower task latency than KVM and CRIU.
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MAP-Law: Coverage-Driven Retrieval Control for Multi-Turn Legal Consultation
MAP-Law dynamically controls retrieval depth in legal AI by computing element coverage, evidence coverage, and marginal gain on a joint node graph, reaching 0.86 element coverage with 58% fewer rounds than fixed baselines on 50 labor-law cases.
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Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution
Solvita is an agentic evolution system using Planner, Solver, Oracle, and Hacker agents with trainable graph knowledge networks updated by reinforcement learning on pass/fail and vulnerability signals to achieve SOTA code generation performance.
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Ask Only When Needed: Proactive Retrieval from Memory and Skills for Experience-Driven Lifelong Agents
ProactAgent treats memory retrieval as a learned policy action and reports large gains in success rate and fewer interaction rounds on SciWorld, AlfWorld, and StuLife.