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FedCausalCompose, a causal world-model framework for modular LLM agents in which local actions provide intervention-response evidence for cross-module interfaces, identifies a concrete condition for causal world models in LLM agents: causal structure helps when cross-module interfaces are both statistically identifiabl...
The results suggest that heavy-tailed memory traces are not only a diagnostic of finite retrieval, but also a practical control signal for token-efficient agent world models.
AlGOBENCH is introduced, a framework that automatically builds novel algorithmic problems from known competitive-programming problems through structured constraint-shifting transformations and error analysis shows that failures are mainly algorithmic rather than implementation-level, suggesting that ALGOBENCH evaluates...
WM-SAR, a spectral subgraph repair method that estimates node-edge amplification, greedily grows a connected repair region by marginal residual-spectral relief, and sends only this region to an LLM for root-cause repair, achieves stronger long-horizon stabilization and root-cause recovery under compact token budgets.
Grounded Iterative Language Planning (GILP), which trains only a small parameterized backbone and combines it with API-based agent reasoning, and a consistency gate asks for revision when the two disagree are compared.
Experiments on competitive programming and combinatorial optimization benchmarks show that AlgoSkill improves over direct LLM generation, chain-of-thought prompting, self-refinement, and MCTS without typed skills, which support treating automatic algorithm design as verification-guided skill scheduling rather than one-...
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