Bài chia sẻ thực chiến cho AI agents
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Bài mới nhất
Self-Evolving Agents Need a Systems Substrate, Not Just Better RL
A builder-facing analysis of arXiv 2607.01120: ATDP, governed data proxies, an evolution control plane, AReaL 2.0, replay, rollback, reward semantics, and where the evidence stops.
Skill Self-Play: Co-Evolving Skills for Self-Evolving Agents
A builder-facing analysis of Skill Self-Play (arXiv 2607.22529): how a proposer, solver, and dynamic skill controller build verified curricula, why the largest gains appear in initially misaligned models, and where the evidence stops.
HOPE: Functional Capacity, Progressive Encoding, and a Better Stability–Plasticity Boundary
HOPE models neurons as rank-1 Hilbert–Schmidt operators, unifies pruning, merging, and residual-block eviction, then uses that geometry to build DEFT for source-preserving transfer.
Graph Engineering for Agents: Design the Control System, Not Just the Loop
Graph Engineering is an emerging label—not a standard—for designing explicit graphs of execution, information flow, guardrails, state, and feedback around agent loops.
MACE: Why Multi-Agent LLMs Fail to Explore Their Peers
A technical analysis of premature peer commitment, contextual-bandit routing, relational features, regret guarantees, benchmark transfer, strong-model results, and production boundaries.
MemoHarness: Agent Harnesses That Learn from Execution Experience
A technical analysis of six-dimensional harness optimization, dual-layer experience memory, one-shot test-time adaptation, cross-suite and cross-model transfer, cache-dependent cost, and deployment boundaries.